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Funnel Activation for Visual Recognition: A New Research Breakthrough

Funnel Activation for Visual Recognition: A New Research Breakthrough

The latest research work in the field of image recognition led to the development of a new activation function for visual recognition tasks, namely Funnel activation(FReLU). In this research ReLU and PReLU are extended to a 2D activation by adding a negligible overhead of spatial condition. Experiments on ImageNet, COCO detection, and semantic segmentation tasks are conducted to measure the performance of FReLU.

CNNs have shown advanced performances in many visual recognition tasks, such as image classification, object detection, and semantic segmentation.  In a CNN framework, basically two major kind of layers play crucial roles, the convolution layer and the non-linear activation layer. Both the convolution layers and activation layers perform distinct functions, however, in both layers there are challenges regarding capturing the spatial dependency. However, despite advancements achieved by complex convolutions, improving the performance of visual tasks is still challenging which results in Rectified Linear Unit (ReLU) being the most widely used function till date.

The research focused on two distinct queries

  1. Could regular convolutions achieve similar accuracy, to grasp the challenging complex images?
    2. Could we design an activation specifically for visual tasks?

1. Effectiveness and generalization performance

In a bid to find answers to these questions, researchers identified spatially insensitiveness in activations to be the main impending factor that prevent visual tasks from improving further.

To address this issue they proposed to find a new visual activation task that could be effective in removing this obstacle and be a better alternative to previous activation approaches.

How other activations work

Taking a look at other activations such as Scalar activations, Contextual conditional activations helps in understanding the context better.

Scalar activations basically are concerned with single input and output which could be represented in form of y = f(x). ReLU or, the Rectified Linear Unit is a widely used activation that is used for various tasks and could be represented as y = max(x, 0).

Contextual conditional activations work on the basis of many-to-one function. In this process neurons that are conditioned on contextual information are activated.

Spatial dependency modeling

In order to accumulate the various ranges of spatial dependences, some approaches utilize various shapes of convolution kernels which leads to lesser efficiency. In other methods like STN, spatial transformations are adaptively used for refining short-range dependencies for the dense vision tasks.

FReLU differs from all other methods in the sense that it performs better without involving complex convolutions. FReLU addresses the issues and solves with a higher level of efficiency.

Receptive field: How FReLU differs from other methods regarding the Receptive field

The size as well as the region of the receptive field play a crucial role in vision recognition tasks. The pixel contribution can be unequal. In order to implement the adaptive receptive field and for a better performance, many methods resort to complex convolutions. FReLU differs from such methods in the way that it achieves the same goal with regular convolutions in a more simple yet highly efficient manner.

Funnel Activation: how funnel activation works

FReLU being conceptually simple is designed for visual tasks. The research further delves into reviewing the ReLU activation and PReLU which is an advanced variant of ReLU, moving on to the key elements of FReLU the funnel condition and the pixel-wise modeling capacity, both of which are not found in ReLU or, in any of its variants.

2. Funnel activation

Funnel condition

Here the same max(·) is adopted as the simple non-linear function, when it comes to the condition part it gets extended to be a 2D condition which is dependent on the spatial context for individual pixel.  For the implementation of the spatial condition, Parametric Pooling Window is used for creating dependency.

Pixel-wise modeling capacity

 Due to the funnel condition the network is capable of generating spatial conditions in the non-linear activations for each pixel. This differs from usual methods where spatial dependency is created in the convolution layer and non-linear transformations are conducted separately. This model achieves a pixel-wise modeling capacity thereby extraction of spatial structure of objects could be addressed naturally.

Experiments

Evaluation of the activation is tested via experiments on ImageNet 2012 classification dataset[9,37].The evaluation is done in stages starting with  different sizes of ResNet. Comparisons with scalar activations is done on ResNets with varying depths, followed by Comparison on light-weight CNNs. An object detection experiment is done to evaluate the generalization performance on various tasks on COCO dataset containing 80 object categories. Further comparison is also done on semantic segmentation task in CityScape dataset. Difference of the images could be perceived through the CityScape images.

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4. Visualization of semantic segmentation

Funnel activation: ablation studies

The scope of the visual activation is tested further via ablation studies where each component of the activation namely 1) funnel condition, and 2)max(·) non-linearity are individually examined. The three parts of the investigation are as follows Ablation on the spatial condition, Ablation on the non-linearity, Ablation on the window size

Compatibility with Existing Methods

Before the new activation could be adopted into the convolutional networks, layers and stages need to be decided, the compatibility with other existing approaches such as SENet also was tested. The process took place in stages as follows

Compatibility with different convolution layers

Compatibility with different stages

Compatibility with SENet

Conclusion: Post all the investigations done to test out the compatibility of FReLU on different levels, it could be stated that this funnel activation is simple yet highly effective and specifically developed for visual tasks.  Its pixel-wise modeling capacity is able to grasp even complex layouts easily. But further research work could be done to expand its scope as it definitely has huge potential.

To get in-depth knowledge regarding the various stages of the research work on Funnel Activation for Visual Recognition, check https://arxiv.org/abs/2007.11824.

 


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5 Most Powerful Computer Vision Techniques in use

5 Most Powerful Computer Vision Techniques in use

Computer Vision is one of the most revolutionary and advanced technologies that deep learning has birthed. It is the computer’s ability to classify and recognize objects in pictures and even videos like the human eye does. There are five main techniques of computer vision that we ought to know about for their amazing technological prowess and ability to ‘see’ and perceive surroundings like we do. Let us see what they are.

Image Classification

The main concern around image classification is categorization of images based on viewpoint variation, image deformation and occlusion, illumination and background clutter. Measuring the accuracy of the description of an image becomes a difficult task because of these factors. Researchers have come up with a novel way to solve the problem.

They use a data driven approach to classify the image. Instead of classifying what each image looks like in code, they feed the computer system with many image classes and then develop algorithms that look at these classes and “learn” about the visual appearance of each class. The most popular system used for image classification is Convolutional Neural Networks (CNNs).

Object Detection

Object detection is, simply put, defining objects within images by outputting bounding boxes and labels or tags for individual objects. This differs from image classification in that it is applied to several objects all at once rather than identifying just one dominant object in an image. Now applying CNNs to this technique will be computationally expensive.

So the technique used for object detection is region-based CNNs of R-CNNs. In this technique, first an image is scanned for objects using an algorithm that generates hundreds of region proposals. Then a CNN is run on each region proposal and only then is each object in each region proposal classified. It is like surveying and labelling the items in a warehouse of a store.

Object Tracking

Object tracking refers to the process of tracking or following a specific object like a car or a person in a given scene in videos. This technique is important for autonomous driving systems in self-driving cars. Object detection can be divided into two main categories – generative method and discriminative method.

The first method uses the generative model to describe the evident characteristics of objects. The second method is used to distinguish between object and background and foreground.

Semantic Segmentation

Crucial to computer vision is the process of segmentation wherein whole images are divided or segmented into pixelgroups that are subsequently labeled and classified.

The science tries to understand the role of each pixel in the image. So, for instance, besides recognizing and detecting a tree in an image, its boundaries are depicted as well. CNNs are best used for this technique.

Instance Segmentation

This method builds on semantic segmentation in that instead of classifying just one single dominant object in an image, it labels multiple images with different colours.

When we see complicated images with multiple overlapping objects and different backgrounds, we apply instance segmentation to it. This is done to generate pixel studies of each object, their boundaries and backdrops.

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Conclusion

Besides these techniques to study and analyse and interpret images or a series of images, there are many more complex techniques that we have not delved into in this blog. However, for more on computer vision, you can peruse the DexLab Analytics website. DexLab Analytics is a premiere Deep Learning training institute In Delhi.

 


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Skills Data Scientists Must Master in 2020

Skills Data Scientists Must Master in 2020

Big data is all around us, be it generated by our news feed or the photos we upload on social media. Data is the new oil and therefore, today, more than ever before, there is a need to study, organize and extract knowledgeable and actionable insights from it. For this, the role of data scientists has become even more crucial to our world. In this article we discuss the various skills, both technical and non-technical a data scientist needs to master to acquire a standing in a competitive market.

Technical Skills

Python and R

Knowledge of these two is imperative for a data scientist to operate. Though organisations might want knowledge of only one of the two programming languages, it is beneficial to know both. Python is becoming more popular with most organisations. Machine Learning using Python is taking the computing world by storm.

GitHub

Git and GitHub are tools for developers and data scientists which greatly help in managing various versions of the software. “They track all changes that are made to a code base and in addition, they add ease in collaboration when multiple developers make changes to the same project at the same time.”

Preparing for Production

Historically, the data scientist was supposed to work in the domain of machine learning. But now data science projects are being more often developed for production systems. “At the same time, advanced types of models now require more and more compute and storage resources, especially when working with deep learning.”

Cloud

Cloud software rules the roost when it comes to data science and machine learning. Keeping your data on cloud vendors like AWS, Microsoft Azure or Google Cloud makes it easily accessible from remote areas and helps quickly set up a machine learning environment. This is not a mandatory skill to have but it is beneficial to be up to date with this very crucial aspect of computing.

Deep Learning

Deep learning, a branch of machine learning, tailored for specific problem domains like image recognition and NLP, is an added advantage and a big plus point to your resume. Even if the data scientist has a broad knowledge of deep learning, “experimenting with an appropriate data set will allow him to understand the steps required if the need arises in the future”. Deep learning training institutes are coming up across the globe, and more so in India.

Math and Statistics

Knowledge of various machine learning techniques, with an emphasis on mathematics and algebra, is integral to being a data scientist. A fundamental grounding in the mathematical foundation for machine learning is critical to a career in data science, especially to avoid “guessing at hyperparameter values when tuning algorithms”. Knowledge of Calculus linear algebra, statistics and probability theory is also imperative.

SQL

Structured Query Language (SQL) is the most widely used database language and a knowledge of the same helps data scientist in acquiring data, especially in cases when a data science project comes in from an enterprise relational database. “In addition, using R packages like sqldf is a great way to query data in a data frame using SQL,” says a report.

AutoML

Data Scientists should have grounding in AutoML tools to give them leverage when it comes to expanding the capabilities of a resource, which could be in short supply. This could deliver positive results for a small team working with limited resources.

Data Visualization

Data visualization is the first step to data storytelling. It helps showcase the brilliance of a data scientist by graphically depicting his or her findings from data sets. This skill is crucial to the success of a data science project. It explains the findings of a project to stakeholders in a visually attractive and non-technical manner.

Non-Technical Skills

Ability to solve business problems

It is of vital importance for a data scientist to have the ability to study business problems in an organization and translate those to actionable data-driven solutions. Knowledge of technical areas like programming and coding is not enough. A data scientist must have a solid foundation in knowledge of organizational problems and workings.

Effective business communication

A data scientist needs to have persuasive and effective communication skills so he or she can face probing stakeholders and meet challenges when it comes to communicating the results of data findings. Soft skills must be developed and inter personal skills must be honed to make you a creatively competent data scientist, something that will set you apart from your peers.

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Agility

Data scientist need to be able to work with Agile methodology in that they should be able to work based on the Scrum method. It improves teamwork and helps all members of the team remain in the loop as does the client. Collaboration with team members towards the sustainable growth of an organization is of utmost importance.

Experimentation

The importance of experimentation cannot be stressed enough in the field of data science. A data scientist must have a penchant for seeking out new data sets and practise robustly with previously unknown data sets. Consider this your pet project and practise on what you are passionate about like sports.


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Deep Learning — Applications and Techniques

Deep Learning — Applications and Techniques

Deep learning is a subset of machine learning, a branch of artificial intelligence that configures computers to perform tasks through experience. While classic machine-learning algorithms solved many problems, they are poor at dealing with soft data such as images, video, sound files, and unstructured text.

Deep-learning algorithms solve the same problem using deep neural networks, a type of software architecture inspired by the human brain (though neural networks are different from biological neurons). Neural Networks are inspired by our understanding of the biology of our brains – all those interconnections between the neurons. But, unlike a biological brain where any neuron can connect to any other neuron within a certain physical distance, these artificial neural networks have discrete layers, connections, and directions of data propagation.

The data is inputted into the first layer of the neural network. In the first layer individual neurons pass the data to a second layer. The second layer of neurons does its task, and so on, until the final layer and the final output is produced. Each neuron assigns a weighting to its input — how correct or incorrect it is relative to the task being performed. The final output is then determined by the total of those weightings.

Deep Learning Use Case Examples

Robotics

Many of the recent developments in robotics have been driven by advances in AI and deep learning. Developments in AI mean we can expect the robots of the future to increasingly be used as human assistants. They will not only be used to understand and answer questions, as some are used today. They will also be able to act on voice commands and gestures, even anticipate a worker’s next move. Today, collaborative robots already work alongside humans, with humans and robots each performing separate tasks that are best suited to their strengths.

Agriculture

AI has the potential to revolutionize farming. Today, deep learning enables farmers to deploy equipment that can see and differentiate between crop plants and weeds. This capability allows weeding machines to selectively spray herbicides on weeds and leave other plants untouched. Farming machines that use deep learning–enabled computer vision can even optimize individual plants in a field by selectively spraying herbicides, fertilizers, fungicides and insecticides.

Medical Imaging and Healthcare

Deep learning has been particularly effective in medical imaging, due to the availability of high-quality data and the ability of convolutional neural networks to classify images. Several vendors have already received FDA approval for deep learning algorithms for diagnostic purposes, including image analysis for oncology and retina diseases. Deep learning is also making significant inroads into improving healthcare quality by predicting medical events from electronic health record data.  Earlier this year, computer scientists at the Massachusetts Institute of Technology (MIT) used deep learning to create a new computer program for detecting breast cancer.

Here are some basic techniques that allow deep learning to solve a variety of problems.

Fully Connected Neural Networks

Fully Connected Feed forward Neural Networks are the standard network architecture used in most basic neural network applications.

Deep Learning — Applications and Techniques

In a fully connected layer each neuron is connected to every neuron in the previous layer, and each connection has its own weight. This is a totally general purpose connection pattern and makes no assumptions about the features in the data. It’s also very expensive in terms of memory (weights) and computation (connections).

Deep Learning — Applications and Techniques

Each neuron in a neural network contains an activation function that changes the output of a neuron given its input. These activation functions are:

  • Linear function: – it is a straight line that essentially multiplies the input by a constant value.
  •  Sigmoid function: – it is an S-shaped curve ranging from 0 to 1.
  • Hyperbolic tangent (tanH) function: – it is an S-shaped curve ranging from -1 to +1
  • Rectified linear unit (ReLU) function: – it is a piecewise function that outputs a 0 if the input is less than a certain value or linear multiple if the input is greater than a certain value.

Each type of activation function has pros and cons, so we use them in various layers in a deep neural network based on the problem. Non-linearity is what allows deep neural networks to model complex functions.

Convolutional Neural Networks

Convolutional Neural Networks (CNN) is a type of deep neural network architecture designed for specific tasks like image classification. CNNs were inspired by the organization of neurons in the visual cortex of the animal brain. As a result, they provide some very interesting features that are useful for processing certain types of data like images, audio and video.

Deep Learning — Applications and Techniques

Mainly three main types of layers are used to build ConvNet architectures: Convolutional Layer, Pooling Layer, and Fully-Connected Layer (exactly as seen in regular Neural Networks). We will stack these layers to form a full ConvNet architecture.  A simple ConvNet for CIFAR-10 classification could have the above architecture [INPUT – CONV – RELU – POOL – FC].

  • INPUT [32x32x3] will hold the raw pixel values of the image, in this case an image of width 32, height 32, and with three color channels R,G,B.
  • CONV layer will compute the output of neurons that are connected to local regions in the input, each computing a dot product between their weights and a small region they are connected to in the input volume. This may result in volume such as [32x32x12] if we decided to use 12 filters.
  • RELU layer will apply an elementwise activation function, such as the max(0,x)max(0,x)thresholding at zero. This leaves the size of the volume unchanged ([32x32x12]).
  • POOL layer will perform a downsampling operation along the spatial dimensions (width, height), resulting in volume such as [16x16x12].
  • FC (i.e. fully-connected) layer will compute the class scores, resulting in volume of size [1x1x10], where each of the 10 numbers correspond to a class score, such as among the 10 categories of CIFAR-10. As with ordinary Neural Networks and as the name implies, each neuron in this layer will be connected to all the numbers in the previous volume.

In this way, ConvNets transform the original image layer by layer from the original pixel values to the final class scores. Note that some layers contain parameters and others don’t. In particular, the CONV/FC layers perform transformations that are a function of not only the activations in the input volume, but also of the parameters (the weights and biases of the neurons). On the other hand, the RELU/POOL layers will implement a fixed function. The parameters in the CONV/FC layers will be trained with gradient descent so that the class scores that the ConvNet computes are consistent with the labels in the training set for each image.

Convolution is a technique that allows us to extract visual features from an image in small chunks. Each neuron in a convolution layer is responsible for a small cluster of neurons in the receding layer. CNNs work well for a variety of tasks including image recognition, image processing, image segmentation, video analysis, and natural language processing.

Recurrent Neural Network

The recurrent neural network (RNN), unlike feed forward neural networks, can operate effectively on sequences of data with variable input length.

The idea behind RNNs is to make use of sequential information. In a traditional neural network we assume that all inputs (and outputs) are independent of each other. But for many tasks that is a very bad idea. If you want to predict the next word in a sentence you better know which words came before it. RNNs are called recurrent because they perform the same task for every element of a sequence, with the output being depended on the previous computations. Another way to think about RNNs is that they have a “memory” which captures information about what has been calculated so far. This is essentially like giving a neural network a short-term memory. This feature makes RNNs very effective for working with sequences of data that occur over time, For example, the time-series data, like changes in stock prices, a sequence of characters, like a stream of characters being typed into a mobile phone.

The two variants on the basic RNN architecture that help solve a common problem with training RNNs are Gated RNNs, and Long Short-Term Memory RNNs (LSTMs). Both of these variants use a form of memory to help make predictions in sequences over time. The main difference between a Gated RNN and an LSTM is that the Gated RNN has two gates to control its memory: an Update gate and a Reset gate, while an LSTM has three gates: an Input gate, an Output gate, and a Forget gate.

RNNs work well for applications that involve a sequence of data that change over time. These applications include natural language processing, speech recognition, language translation, image captioning and conversation modeling.

Conclusion

So this article was about various Deep Learning techniques. Each technique is useful in its own way and is put to practical use in various applications daily. Although deep learning is currently the most advanced artificial intelligence technique, it is not the AI industry’s final destination. The evolution of deep learning and neural networks might give us totally new architectures. Which is why more and more institutes are offering courses on AI and Deep Learning across the world and in India as well. One of the best and most competent artificial intelligence certification in Delhi NCR is DexLab Analytics. It offers an array of courses worth exploring.


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Deep Learning and Computer Vision – A study – Part II

Deep Learning and Computer Vision – A study – Part II

In the first series of this article we have seen what is computer vision and a brief review of its applications. You can read the first part of this article here. We have also seen the contribution of deep learning in computer vision. Especially we focused on Image Classification and deep learning architecture which is used in Image Classification. In this series we will focus on other applications including Image Localization, Object Detection and Image Segmentation. We will also walk through the required deep learning architecture used for above applications.

Image classification with Localization

Similar to classification, localization finds the location of a single object inside the image. Localization can be used for lots of useful real-life problems. For example, smart cropping (knowing where to crop images based on where the object is located), or even regular object extraction for further processing using different techniques. It can be combined with classification for not only locating the object but categorizing it into one of many possible categories.

A classical dataset for image classification with localization is the PASCAL Visual Object Classes datasets, or PASCAL VOC for short (e.g. VOC 2012). These are datasets used in computer vision challenges over many years.

Object detection

Iterating over the problem of localization plus classification we end up with the need for detecting and classifying multiple objects at the same time. Object detection is the problem of finding and classifying a variable number of objects on an image. The important difference is the “variable” part. In contrast with problems like classification, the output of object detection is variable in length, since the number of objects detected may change from image to image.

The PASCAL Visual Object Classes datasets, or PASCAL VOC for short (e.g. VOC 2012), is a common dataset for object detection.

Deep learning for Image Localization and Object Detection

There is nothing hardcore about the architectures which we are going to discuss. What we are going to discuss are some clever ideas to make the system intolerant to the number of outputs and to reduce its computation cost. So, we do not know the exact number of objects in our image and we want to classify all of them and draw a bounding box around them. That means that the number of coordinates that the model should output is not constant. If the image has 2 objects, we need 8 coordinates. If it has 4 objects, we want 16. So how we build such a model?

One key idea to traditional computer vision is regions proposal. We generate a set of windows that are likely to contain an object using classic CV algorithms, like edge and shape detection and we apply only these windows (or regions of interests) to the CNN. To learn more about how regions are proposed, we introduce a new architecture called RCNN.

R-CNN

Given an image with multiple objects, we generate some regions of interests using a proposal method (in RCNN’s case this method is called selective search) and wrap the regions into a fixed size. We forward each region to Convolutional Neural Network (such as AlexNet), which will use an SVM to make a classification decision for each one and predicts a regression for each bounding box. This prediction comes as a correction of the region proposed, which may be in the right position but not at the exact size and orientation.

Although the model produces good results, it suffers from a major issue. It is quite slow and computationally expensive. Imagine that in an average case, we produce 2000 regions, which we need to store in disk, and we forward each one of them into the CNN for multiple passes until it is trained. To fix some of these problems, an improvement of the model comes in play called ‘Fast-RCNN’

Fast RCNN

The idea is straightforward. Instead of passing all regions into the convolutional layer one by one, we pass the entire image once and produce a feature map. Then we take the region proposals as before (using some external method) and sort of project them onto the feature map. Now we have the regions in the feature map instead of the original image and we can forward them in some fully connected layers to output the classification decision and the bounding box correction.

Note that the projection of regions proposal is implemented using a special layer (ROI layer), which is essentially a type of max-pooling with a pool size dependent on the input, so that the output always has the same size.

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Faster RCNN

And we can take this a step further. Using the produced feature maps from the convolutional layer, we infer regions proposal using a Region Proposal network rather than relying on an external system. Once we have those proposals, the remaining procedure is the same as Fast-RCNN (forward to ROI layer, classify using SVM and predict the bounding box). The tricky part is how to train the whole model as we have multiple tasks that need to be addressed:

  • The region proposal network should decide for each region if it contains an object or not.
  • It needs to produce the bounding box coordinates.
  • The entire model should classify the objects to categories.
  • And again predict the bounding box offsets.

As the name suggests, Faster RCNN turns out to be much faster than the previous models and is the one preferred in most real-world applications.

Localization and object detection is a super active and interesting area of research due to the high emergency of real world applications that require excellent performance in computer vision tasks (self-driving cars, robotics). Companies and universities come up with new ideas on how to improve the accuracy on regular basis.

There is another class of models for localization and object detection, called single shot detectors, which have become very popular in the last few years because they are even faster and require less computational cost in general. Sure, they are less accurate, but they are ideal for embedded systems and similar power-hungry applications.

Object segmentation

Going one step further from object detection we would want to not only find objects inside an image, but find a pixel by pixel mask of each of the detected objects. We refer to this problem as instance or object segmentation.

Semantic Segmentation is the process of assigning a label to every pixel in the image. This is in stark contrast to classification, where a single label is assigned to the entire picture. Semantic segmentation treats multiple objects of the same class as a single entity. On the other hand, instance segmentation treats multiple objects of the same class as distinct individual objects (or instances). Typically, instance segmentation is harder than semantic segmentation.

In order to perform semantic segmentation, a higher level understanding of the image is required. The algorithm should figure out the objects present and also the pixels which correspond to the object. Semantic segmentation is one of the essential tasks for complete scene understanding. This can be used in analysis of medical images and satellite images. Again, the VOC 2012 and MS COCO datasets can be used for object segmentation.

Deep Learning for Image Segmentation

Modern image segmentation techniques are powered by deep learning technology. Here are several deep learning architectures used for segmentation.

Convolutional Neural Networks (CNNs) 

Image segmentation with CNN involves feeding segments of an image as input to a convolutional neural network, which labels the pixels. The CNN cannot process the whole image at once. It scans the image, looking at a small “filter” of several pixels each time until it has mapped the entire image. To learn more see our in-depth guide about Convolutional Neural Networks.

Fully Convolutional Networks (FCNs)

Traditional CNNs have fully-connected layers, which can’t manage different input sizes. FCNs use convolutional layers to process varying input sizes and can work faster. The final output layer has a large receptive field and corresponds to the height and width of the image, while the number of channels corresponds to the number of classes. The convolutional layers classify every pixel to determine the context of the image, including the location of objects.

DeepLab

One main motivation for DeepLab is to perform image segmentation while helping control signal decimation—reducing the number of samples and the amount of data that the network must process. Another motivation is to enable multi-scale contextual feature learning—aggregating features from images at different scales. DeepLab uses an ImageNet pre-trained residual neural network (ResNet) for feature extraction.   DeepLab uses atrous (dilated) convolutions instead of regular convolutions. The varying dilation rates of each convolution enable the ResNet block to capture multi-scale contextual information. DeepLab comprises three components:

  • Atrous convolutions—with a factor that expands or contracts the convolutional filter’s field of view.
  • ResNet—a deep convolutional network (DCNN) from Microsoft. It provides a framework that enables training thousands of layers while maintaining performance. The powerful representational ability of ResNet boosts computer vision applications like object detection and face recognition.
  • Atrous spatial pyramid pooling (ASPP)—provides multi-scale information. It uses a set of atrous convolutions with varying dilation rates to capture long-range context. ASPP also uses global average pooling (GAP) to incorporate image-level features and add global context information.

SegNet neural network

An architecture based on deep encoders and decoders is also known as semantic pixel-wise segmentation. It involves encoding the input image into low dimensions and then recovering it with orientation invariance capabilities in the decoder. This generates a segmented image at the decoder end.

Conclusion

In this post we have discussed some applications of computer vision including Image Localization, Object Detection and Image Segmentation. We then discussed required deep learning architectures which are used for the above applications.


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Commercial Uses of Deep Learning

Commercial Uses of Deep Learning

Deep Learning has its limitations, scientists argue.

“We have machines that learn in a very narrow way,” Yoshua Bengio, deep learning pioneer, said in his keynote address at NeurIPS in December, 2019. “They need much more data to learn a task than human examples of intelligence, and they still make stupid mistakes.”

Unarguably, deep learning is an imperfect framework of intelligence. It does not think abstractedly, does not comprehend causation and struggles with out-of-distribution generalization. For a deeper understanding of its limitations, this brilliant paper on the science and its shortcomings is available on the internet.

However, despite numerous shortcomings, the commercial uses of deep learning are only just being mined and its capabilities to automate and transform industries still abound. AI and deep learning capabilities, as developed as they are today, are sufficiently mature to spearhead transformation, innovation, and value creation across industries like agriculture, healthcare and construction. “For the most part, these transformative opportunities have not yet been operationalized at scale.”

Radiology

For instance, in the radiology industry, something as extreme and point blank as this was declared in 2016 by AI luminary Geoff Hinton – “It’s quite obvious that we should stop training radiologists now.” Hinton’s comments drew worked up reactions in the medical community but his statement was based on strong data which showed neural networks can identify medical conditions from X-rays with better accuracy than human radiologists can.

Yet, years after Hinton foresaw the removal of the need of human radiologists from the medical science field, no clinic in the world has deployed AI-driven radiology tools at scale. Only a few health organizations have begun using it in limited settings. But more and more organizations are slowly adopting deep learning in radiology.

Off Road Autonomous Vehicles

In another instance, the off-road autonomous vehicle industry is seeing a slow move towards tapping the massive unrealized commercial potential of AI. Construction, agriculture and mining are some of the largest industries in the world. If these industries start deploying deep learning powered automated machines to do work that human hands are trained to do, a massive pool of cost, productivity and safety benefits could be tapped.

Energy

In the field of energy, leading players like BP are using deep learning to innovate and transform work conditions on site. “It uses technology to drive new levels of performance, improve the use of resources and safety and reliability of oil and gas production and refining. From sensors that relay the conditions at each site to using AI technology to improve operations, BP puts data at the fingertips of engineers, scientists and decision-makers to help drive high performance.”

Retail

Burberry, a luxury fashion brand, uses big data and AI to fight counterfeit products. It is also trying to enhance sales and customer relationships by initiating a loyalty program that creates data to help personalize the shopping experience for each customer.

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Social Media

Both Twitter and Facebook are tapping into structured and unstructured sets of big data for understanding user behavior and using deep learning to check for communal or racist comments and user preferences.

Deep Learning and Artificial Intelligence is the future and it is here to stay. No wonder then, that more and more professionals are opting to train themselves through deep learning courses. DexLab Analytics is one of the best Deep Learning training institutes in Delhi. Do go through its website for more details.

 

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How AI and Deep Learning Helps In Weather Forecasting

 

How AI and Deep Learning Helps In Weather Forecasting

The world’s fight against extreme weather conditions and climate change is at the forefront of all discussions and debates on the environment. In fact, climate change is the biggest concern we are faced with today, and studying the climate has increasingly become the primary preoccupation of scientists and researchers. They have received a shot in the arm with the increase in the scope of artificial intelligence and deep learning in predicting weather patterns.

Take for instance the super cyclone Amphan that has ravaged West Bengal and Orissa. Had it not been for weather forecasting techniques, meteorologists would never had predicted the severity of the cyclone and the precautionary evacuation of thousands of people from coastal areas would not have been taken, leading to massive loss of lives. This is where the importance of weather forecasting lies.

Digitizing the prediction model

Traditionally, weather forecasting depends on a combination of observations of the current state of the weather and data sets from previous observations. Meteorologists prepare weather forecasts collecting a wealth of data and running it through prediction models. These sets of data come from hundreds of observations like temperature, wind speed, and precipitation produced by weather stations and satellites across the globe. Due to the digitization of these weather models, accuracy has improved much more than it was a few decades ago. And with the recent introduction of machine learning, forecasting has become an even more accurate and exact science.

Machine Learning

Machine learning can be utilized to make comparisons between historical weather forecasts and observations in real time. Also, machine learning can be used to make models account for inaccuracies in predictions, like overestimated rainfall.

At weather forecast institutions, prediction models use gradient boosting that is a machine learning technique for building predictive models. This is used to correct any errors that come into play with traditional weather forecasting.

Deep Learning

Machine Learning and Deep Learning are increasingly being used for nowcasting, a model of forecasting in the real time, traditionally within a two-hour time span. It provides precipitation forecasts by the minute. With deep learning, a meteorologist can anywhere in the vicinity of a weather satellite (which runs on deep learning technology) use nowcasting rather than just those who live near radar stations (which are used in traditional forecasting).

Extreme Weather Events

Deep learning is being used not only for predicting usual weather patterns, it is being used to predict extreme weather conditions as well. Rice University engineers have designed a deep learning computer system that has trained itself to predict, in accurate terms, extreme weather conditions like heat waves or cold waves. The computer system can do so up to five days in advance. And the most fascinating part is it uses the least information about current weather conditions to make predictions.

This system could effectively guide NWP (numerical weather prediction) that currently does not have the ability to predict extreme weather conditions like heat waves. And it could be a super cheap way to do so as well.

According to sciencedaily.com, with further development, the system could serve as an early warning system for weather forecasters, and as a tool for learning more about the atmospheric conditions that lead to extreme weather, said Rice’s Pedram Hassanzadeh, co-author of a study about the system published online in the American Geophysical Union’s Journal of Advances in Modeling Earth Systems.

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Thus, it is no surprise then that machine learning and deep learning is being widely adopted the world over. In India, is it being taken up as a form of study and training in metropolitans like Delhi and Gurgaon. For the best Machine Learning course in Delhi and deep learning course in delhi, check out the DexLab Analytics website today.

 

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Python Statistics Fundamentals: How to Describe Your Data? (Part II)

Python Statistics Fundamentals: How to Describe Your Data? (Part II)

In the first part of this article, we have seen how to describe and summarize datasets and how to calculate types of measures in descriptive statistics in Python. It’s possible to get descriptive statistics with pure Python code, but that’s rarely necessary.

Python is an advanced programming language extensively used in all of the latest technologies of Data Science, Deep Learning and Machine learning. Furthermore, it is particularly responsible for the growth of the Machine Learning course in IndiaMoreover, numerous courses like Deep Learning for Computer vision with Python, Text Mining with Python course and Retail Analytics using Python are pacing up with the call of the age. You must also be in line with the cutting-edge technologies by enrolling with the best Python training institute in Delhi now, not to regret it later.

In this part, we will see the Python statistics libraries which are comprehensive, popular, and widely used especially for this purpose. These libraries give users the necessary functionality when crunching data. Below are the major Python libraries that are used for working with data.

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NumPy and SciPy – Fundamental Scientific Computing

NumPy stands for Numerical Python. The most powerful feature of NumPy is the n-dimensional array. This library also contains basic linear algebra functions, Fourier transforms, advanced random number capabilities. NumPy is much faster than the native Python code due to the vectorized implementation of its methods and the fact that many of its core routines are written in C (based on the CPython framework).

For example, let’s create a NumPy array and compute basic descriptive statistics like mean, median, standard deviation, quantiles, etc.

SciPy stands for Scientific Python, which is built on NumPy. NumPy arrays are used as the basic data structure by SciPy.

Scipy is one of the most useful libraries for a variety of high-level science and engineering modules like discrete Fourier transforms, Linear Algebra, Optimization and Sparse matrices. Specifically in statistical modelling, SciPy boasts of a large collection of fast, powerful, and flexible methods and classes. It can run popular statistical tests such as t-test, chi-square, Kolmogorov-Smirnov, Mann-Whitney rank test, Wilcoxon rank-sum, etc. It can also perform correlation computations, such as Pearson’s coefficient, ANOVA, Theil-Sen estimation, etc.

Pandas – Data Manipulation and Analysis

Pandas library is used for structured data operations and manipulations. It is extensively used for data preparation. The DataFrame() function in Pandas takes a list of values and outputs them in a table. Seeing data enumerated in a table gives a visual description of a data set and allows for the formulation of research questions on the data.

The describe() function outputs various descriptive statistics values, except for the variance. The variance is calculated using the var() function in Pandas.

The mean() function, returns the mean of the values for the requested axis.

Matplotlib – Plotting and Visualization

Matplotlib is a Python library for creating 2D plots. It is used for plotting a wide variety of graphs, starting from histograms to line plots to heat plots. One can use Pylab feature in IPython notebook (IPython notebook –pylab = inline) to use these plotting features inline. If the inline option is ignored, then pylab converts IPython environment to an environment, very similar to Matlab.

matplotlib.pylot is a collection of command style functions.

If a single list array is provided to the plot() command, matplotlib assumes it is a sequence of Y values and internally generates the X value for you.

Each function makes some change to a figure, like creating a figure, creating a plotting area in a figure, decorating the plot with labels, etc. Now, let us create a very simple plot for some given data, as shown below:

Scikit-learn – Machine Learning and Data Mining

Scikit-learn built on NumPy, SciPy and matplotlib. Scikit-learn is the most widely used Python library for classical machine learning. But, it is necessary to include it in the discussion of statistical modeling as many classical machine learning (i.e. non-deep learning) algorithms can be classified as statistical learning techniques. This library contains a lot of efficient tools for machine learning and statistical modeling including classification, regression, clustering and dimensional reduction.

Conclusion

In this article, we covered a set of Python open-source libraries that form the foundation of statistical modelling, analysis, and visualization. On the data side, these libraries work seamlessly with the other data analytics and data engineering platforms, such as Pandas and Spark (through PySpark). For advanced machine learning tasks (e.g. deep learning), NumPy knowledge is directly transferable and applicable in popular packages such as TensorFlow and PyTorch. On the visual side, libraries like Matplotlib, integrate nicely with advanced dashboarding libraries like Bokeh and Plotly.

 

https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html

 

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Deep Learning and its Progress as Discussed at Intel’s AI Summit

Deep Learning and its Progress as Discussed at Intel’s AI Summit

At the latest AI summit organized by Intel, Mr. Naveen Rao, Vice President and General Manager of Intel’s AI Products Group, focused on the most vibrant age of computing that is the present age we are living. According to Rao, the widespread and sudden growth of neural networks is putting the capability of the hardware into a real test. Therefore, we now have to reflect deeply on “how processing, network, and memory work together” to figure a pragmatic solution, he said.

The storage of data has seen countless improvements in the last 20 years. We can now boast of our prowess of handling considerably large sets of data, with greater computing capability in a single place. This led to the expansion of the neural network models with an eye on the overall progress in neural Network Machine Learning Python and computing in general.

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With the onset of exceedingly large data sets to work with, Deep learning for Computer Vision Course and the other models of Deep Learning to recognize speech, images, and text are extensively feeding on them. The technological giants were undoubtedly the early birds to grab the technical: the hardware and the software configuration to have an edge on the others. 

Surely, Deep Learning is on its peak now, where computers can identify the images with incredible vividness. On the other hand, chatbots can carry on with almost natural conversations with us. It is no wonder that the Deep learning Training Institutes all over the world are jumping in the race to bring all of these new technologies efficiently to the general mass.

The Big Problem

We are living in the dynamic age of AI and Machine Learning, with the biggies like Google, Facebook, and its peers, having the technical skills and configuration to take up the challenges. However, the neural networks have fattened up so much lately that it has already started to give the hardware a tough time, getting the better of them all the time.

Deep Learning and AI using Python

The number of parameters of the Neural network models is increasing as never before. They are “actually increasing on the order of 10x year on year”, as per Rao. Thus, it is a wall looming in AI. Though Intel is trying its best to tackle this obvious wall, which might otherwise give the industry a severe setback, with extensive research to bring new chip architectures and memory technologies into play, it cannot solve the AI processing problem single-handedly. Rao concluded on a note of requesting the partners in the present competitive scenario.

 

Sourced from: www.datanami.com/2019/11/13/deep-learning-has-hit-a-wall-intels-rao-says

 

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