This beginner’s guide explains the concepts of deep learning and computer vision. Also get insights into 5 interesting applications of deep learning for computer vision.
Deep learning and computer vision are trends at the forefront of computational, engineering, and statistical innovation. You’ve probably heard a lot about these trends if you follow technology blogs and news reports, however, it’s easy to get lost in the terminology without proper explanations.
This beginner’s guide explains the concepts of deep learning and computer vision. You’ll also get insights into five interesting applications of deep learning for computer vision.
To truly understand deep learning, the following definitions are important:
Bearing these definitions in mind, deep learning is a subset of machine learning in which machines use deep neural network architecture and algorithms to learn tasks autonomously.
What distinguishes deep learning is that its networks contain many hidden layers. This extra complexity empowers machines to learn from unstructured, unlabeled data as well as labeled and categorized data.
Note that none of these concepts are particularly new — rapid advances in computing power and technology enables the models to be fed with large volumes of data. The more data available, the more proficient the models become at learning tasks.
Speech recognition, image recognition, natural language processing (NLP), and computer vision are some of the areas deep learning has improved dramatically.
Many technology companies now specialize in providing platforms for training deep learning models in computer vision and other areas. Such companies have also facilitated further innovation in these artificial intelligence branches.
Computer vision is a scientific field spanning multiple disciplines that is concerned with getting computers to extract high-level meaning from images and videos.
The list of applications of computer vision is extensive; some of the most interesting include:
Deep learning has several uses in helping to achieve computer vision and overcoming its challenges — here are five of them.
Probably the computer vision capability familiar to most people is facial recognition, which is a common feature in today’s smartphones and cameras. Modern facial recognition systems at large enterprises are powered by deep learning networks and algorithms.
Facebook’s DeepFace identifies human faces in digital images using a nine-layer neural network. The system has 97 percent accuracy, which is famously better than the FBI’s facial recognition system. Google also developed its own highly accurate facial recognition system named FaceNet.
Classification with localization means identifying objects of a certain class in images and videos and highlighting their location, typically by drawing a box around the object. This particular computer vision use case is more challenging than simple object classification, which assigns labels to entire images (e.g. cat, bird, dog).
Classification with localization is particularly helpful in the medical field because healthcare organizations can train neural networks to rapidly identify cancerous regions of the body based on x-rays and other diagnostic medical images.
An extension of object classification and localization is object detection, in which the model can identify many objects of different types in images.
Semantic segmentation is a more advanced form of image classification and localization made possible by neural networks. With semantic segmentation, a model can classify and locate all of the pixels in an image or video. See the gif below to view semantic segmentation in action.
*Image source: *https://nikolasent.github.io/proj/proj4
The most exciting potential use for this computer vision function is real-time semantic segmentation used by self-driving cars. Identifying and localizing objects accurately can improve the safety and reliability of autonomous vehicles.
Colorization is the process of converting grayscale images to full-color images. The excitement of this use case comes from its aesthetic appeal. Colorization with deep learning can give new context and vibrancy to old black and white movies and photos. Check out this article for some impressive examples of image colorization using deep learning.
Technology giant Nvidia sent the Internet into a frenzy in 2018 when it announced a new technique that can reconstruct corrupted images. Wear and tear on old printed photographs can lead to holes, blurring, and other damage to the image. Digital images can get damaged and lose some of their pixels due to corrupt memory cards.
The technique uses deep learning to fill in the missing parts of images. According to the research paper, the deep learning model used by Nvidia can “robustly handle holes of any shape, size, location, or distance from the image borders”.
You’ve read about just a small sample of a wide range of exciting uses and applications of deep learning for computer vision. You’ve also got a beginner’s guide to understanding deep learning and computer vision.
In today’s world, Computer Vision technologies are everywhere. They are embedded within many of the tools and applications that we use on a daily basis. However, we often pay little attention to those underlaying Computer Vision technologies because they tend to run in the background. As a result, only a small fraction of those outside the tech industries know about the importance of those technologies. Therefore, the goal of this article is to provide an overview of Computer Vision to those with little to no knowledge about the field. I attempt to achieve this goal by answering three questions: What is Computer Vision?, Why should you learn Computer Vision? and How you can get started?
Figure 1: Portrait of Larry Roberts.
The field of Computer Vision dates back to the 1960s when Larry Roberts, who is now widely considered as the “Father of Computer Vision”, published his paper _Machine Perception of Three-Dimensional Solids _detailing how a computer can infer 3D shapes from a 2D image (Roberts, 1995). Since then, other researchers have made amazing contributions to the field. These advances, however, have not changed the underlaying goal of Computer Vision which is to mimic the human visual system. From an engineering point of view, this means being able to build autonomous systems that can do things a human visual system can do such as detecting and recognizing objects, recognizing faces and facial expressions, etc. (Huang, 1996). Traditionally, many approaches in Computer Vision involves manual feature extraction. This means manually finding some unique features/characteristics (edges, shapes, etc) that are only present in an object to be able to detect and recognize what that object is. Unfortunately, one major issue arises when trying to detect and recognize variations (sizes, lightning conditions, etc) of that same object. It is difficult to find features that can uniquely identify an object across all variations. Fortunately, this problem is now solved with the introduction of Machine Learning, particularly a sub-field of Machine Learning called Deep Learning. Deep Learning utilizes a form of Neural Networks called Convolutional Neural Networks (CNNs). Unlike the traditional methods, methods that utilize CNNs are able to extract features automatically. Instead of trying to figure out which features can represent an object manually, a CNN can learn those features automatically by looking at many variations of that same object. As result, many recent advancements in the field of Computer Vision involves the use of CNNs.
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In the previous blog, we looked into the fact why Few Shot Learning is essential and what are the applications of it. In this article, I will be explaining the Relation Network for Few-Shot Classification (especially for image classification) in the simplest way possible. Moreover, I will be analyzing the Relation Network in terms of:
Moreover, effectiveness will be evaluated on the accuracy, time required for training, and the number of required training parameters.
Please watch the GitHub repository to check out the implementations and keep updated with further experiments.
In few shot classification, our objective is to design a method which can identify any object images by analyzing few sample images of the same class. Let’s the take one example to understand this. Suppose Bob has a client project to design a 5 class classifier, where 5 classes can be anything and these 5 classes can even change with time. As discussed in previous blog, collecting the huge amount of data is very tedious task. Hence, in such cases, Bob will rely upon few shot classification methods where his client can give few set of example images for each classes and after that his system can perform classification young these examples with or without the need of additional training.
In general, in few shot classification four terminologies (N way, K shot, support set, and query set) are used.
At this point, someone new to this concept will have doubt regarding the need of support and query set. So, let’s understand it intuitively. Whenever humans sees any object for the first time, we get the rough idea about that object. Now, in future if we see the same object second time then we will compare it with the image stored in memory from the when we see it for the first time. This applied to all of our surroundings things whether we see, read, or hear. Similarly, to recognise new images from query set, we will provide our model a set of examples i.e., support set to compare.
And this is the basic concept behind Relation Network as well. In next sections, I will be giving the rough idea behind Relation Network and I will be performing different experiments on 102-flower dataset.
The Core idea behind Relation Network is to learn the generalized image representations for each classes using support set such that we can compare lower dimensional representation of query images with each of the class representations. And based on this comparison decide the class of each query images. Relation Network has two modules which allows us to perform above two tasks:
We can define the whole procedure in just 5 steps.
Few things to know during the training is that we will use only images from the set of selective class, and during the testing, we will be using images from unseen classes. For example, from the 102-flower dataset, we will use 50% classes for training, and rest will be used for validation and testing. Moreover, in each episode, we will randomly select 5 classes to create the support and query set and follow the above 5 steps.
That is all need to know about the implementation point of view. Although the whole process is simple and easy to understand, I’ll recommend reading the published research paper, Learning to Compare: Relation Network for Few-Shot Learning, for better understanding.
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The Deep Learning DevCon 2020, DLDC 2020, has exciting talks and sessions around the latest developments in the field of deep learning, that will not only be interesting for professionals of this field but also for the enthusiasts who are willing to make a career in the field of deep learning. The two-day conference scheduled for 29th and 30th October will host paper presentations, tech talks, workshops that will uncover some interesting developments as well as the latest research and advancement of this area. Further to this, with deep learning gaining massive traction, this conference will highlight some fascinating use cases across the world.
Here are ten interesting talks and sessions of DLDC 2020 that one should definitely attend:
Also Read: Why Deep Learning DevCon Comes At The Right Time
By Dipanjan Sarkar
**About: **Adversarial Robustness in Deep Learning is a session presented by Dipanjan Sarkar, a Data Science Lead at Applied Materials, as well as a Google Developer Expert in Machine Learning. In this session, he will focus on the adversarial robustness in the field of deep learning, where he talks about its importance, different types of adversarial attacks, and will showcase some ways to train the neural networks with adversarial realisation. Considering abstract deep learning has brought us tremendous achievements in the fields of computer vision and natural language processing, this talk will be really interesting for people working in this area. With this session, the attendees will have a comprehensive understanding of adversarial perturbations in the field of deep learning and ways to deal with them with common recipes.
Read an interview with Dipanjan Sarkar.
By Divye Singh
**About: **Imbalance Handling with Combination of Deep Variational Autoencoder and NEATER is a paper presentation by Divye Singh, who has a masters in technology degree in Mathematical Modeling and Simulation and has the interest to research in the field of artificial intelligence, learning-based systems, machine learning, etc. In this paper presentation, he will talk about the common problem of class imbalance in medical diagnosis and anomaly detection, and how the problem can be solved with a deep learning framework. The talk focuses on the paper, where he has proposed a synergistic over-sampling method generating informative synthetic minority class data by filtering the noise from the over-sampled examples. Further, he will also showcase the experimental results on several real-life imbalanced datasets to prove the effectiveness of the proposed method for binary classification problems.
By Dongsuk Hong
About: This is a paper presentation given by Dongsuk Hong, who is a PhD in Computer Science, and works in the big data centre of Korea Credit Information Services. This talk will introduce the attendees with machine learning and deep learning models for predicting self-employment default rates using credit information. He will talk about the study, where the DNN model is implemented for two purposes — a sub-model for the selection of credit information variables; and works for cascading to the final model that predicts default rates. Hong’s main research area is data analysis of credit information, where she is particularly interested in evaluating the performance of prediction models based on machine learning and deep learning. This talk will be interesting for the deep learning practitioners who are willing to make a career in this field.
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ML is type of AI
AI is a discipline , Machine Learning is tool set to achieve AI. DL is type of ML when data is unstructured like image, speech , video etc.
AI & ML was daunting and with high barrier to entry until cloud become more robust and natural AI platform. Entry barrier to AI & ML has fallen significantly due to
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