As many of us are no doubt aware, the invariable progress made in the field of Computer Vision, has lead to some incredible achievements and broad deployment in fields from healthcare and self-driving cars, to climate study, to name but a few. From state-of-the-art Liquid Nitrogen cooled hardware in the form of Tensor Processing Units (TPU) to increasingly sophisticated, multi-million parameter Deep-Convolutional Networks such as GoogLeNet, AlexNet the capability of such technology continues to break previously unassailable barriers.
Despite these incredible achievements, however, it has been proven that even the most skilful models are not infallible. Multiple research efforts have demonstrated how sensitive these models are to even imperceivably small changes in the** input data structure**. Initially in the findings of the joint research paper by Google and New York University: ‘Intriguing properties of neural networks, 2014’ the subject of model vulnerability to adversarial examples is now recognised as a subject of such importance that competitions now exist to tackle it:
The existence of these errors raises a variety of questions about out-of-sample generalization, and how the use of such examples might be used to abuse deployed systems.
In some applications, these errors might not arise intentionally, moreover, they can arise as a result of human error or simply as a result of input instability. In the mining industry, computer vision has innumerable, highly useful applications, from streaming processing plant conveyor belt imagery in order to predict ore purity for example, to detecting commodity stockpile levels and illegal shipping/mining using satellite imagery.
Quite often we find that such image data is corrupted during collection, as a result of camera misalignment, vibrations or simply very unique out-of-sample examples that can lead to misclassification.
In order to overcome examples such as these and generally improve our models against corrupt or perturbed data, we can employ a form of Neural Structured Learning called Adversarial Regularization.
Neural Structured Learning (NSL)is a relatively new, open-source framework developed by the good folks at TensorFlow for training deep neural networks with structured signals (as opposed to the conventional single sample). NSL implements Neural Graph Learning, in which a neural network is trained using graphs (see image below) which carry information about both a target (node) and neighbouring information in other nodes connected via node edges.
In doing so, this allows the trained model to simultaneously exploit both labelled and unlabelled data through:
#data-science #tensorflow #machine-learning #computer-vision #convolutional-network
Recently, researchers from Google proposed the solution of a very fundamental question in the machine learning community — What is being transferred in Transfer Learning? They explained various tools and analyses to address the fundamental question.
The ability to transfer the domain knowledge of one machine in which it is trained on to another where the data is usually scarce is one of the desired capabilities for machines. Researchers around the globe have been using transfer learning in various deep learning applications, including object detection, image classification, medical imaging tasks, among others.
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Neural Networks are great function approximators and feature extractors but sometimes their weights become too specialized and cause overfitting. That’s where the concept of Regularization comes into picture which we will discuss along with slight differences between two major weight regularization techniques which are mistakenly considered the same.
Neural Networks was first introduced in 1943 by Warren McCulloch and Walter Pitts but weren’t popular enough as they required large amounts of data and computation power which were not feasible at that time. But as the above constraints became feasible along with other training advancements such as parameter initialization and better activation functions, they again started to dominate the various competitions and found applications in various human assistive technologies.
Today Neural Networks form the backbone of many famous applications like Self-Driving Car, Google Translate, Facial Recognition Systems etc and are applied in almost all technologies used by evolving human race.
Neural Networks are very good at approximating functions be linear or non-linear and are also terrific when extracting features from the input data. This capability makes them perform wonders over a large range of tasks be it computer vision domain or language modelling. But as we all have heard the famous saying :
“With Great Power Comes Great Responsibility”.
This saying also applies to the all-mighty neural nets. Their power of being great function approximators sometimes causes them to overfit the dataset by approximating a function which will perform extremely well on the data on which it was trained on but fails miserably when tested on a data it hasn’t seen before. To be more technical, the neural networks learn weights which are more specialized on the given data and fails to learn features which can be generalized.
To solve the problem of overfitting, a class of techniques known as Regularization is applied to reduce the complexity of the model and constraint weights in a manner which forces the neural network to learn generalizable features.
Regularization may be defined as any change we make to the training algorithm in order to reduce the generalization error but not the training error. There are many regularization strategies. Some put extra constraints on the models such as adding constraints to parameter values while some add extra terms to the objective function which can be thought as adding indirect or soft constraints on the parameter values. If we use these techniques carefully, this can lead to improved performance on the test set. In the context of deep learning, most regularization techniques are based on regularizing the estimators. While regularizing an estimator, there is a tradeoff where we have to choose a model with increased bias and reduced variance. An effective regularizer is one which makes a profitable trade, reducing variance significantly while not overly increasing the bias.
The major regularization techniques used in practice are:
In this post, we mainly focus on L2 Regularization and argue whether we can refer L2 regularization and weight decay as two faces of the same coin.
#regularization #computer-vision #neural-networks #deep-learning #machine-learning #deep learning
Emphasis on Weight Decay using TensorFlow 2.0
In this era of the information superhighway, the world around us is changing for good and it would not be an overstatement to say that Deep Learning is the next transformation. Deep Learning is a set of powerful mathematical tools that enable us, to represent, interpret, and control the complex world around us.
The programming paradigm is changing: Instead of programming a computer, we teach it to learn something and it does what we want.
This particular notion is extremely captivating and has driven machine learning practitioners to develop models to take these concepts and ideas further along and apply them in real-world scenarios.
However, the fundamental problem in building sophisticated machine learning models is how to make the architecture to do good not just on the training data but also on the testing data, i.e., on previously unseen features. In order to overcome this central problem, we must modify the model by applying certain strategies to reduce the test error, possibly at the expense of increased training error. These strategies are collectively known as regularization techniques. Nevertheless, regularization is useful only when the model is suffering from a high variance problem where the network overfits to the training data but fails to generalize new features (validation or test data).
One of the most extensively used regularization strategies is the utilization of the L2 norm. This can be implemented by adding a regularization term to the cost function of the network.
Cost function with regularization term
The first part of the equation corresponds to the computation of net network loss. The terms _w _& _b _represents the weights and biases that the model has learned. The second part corresponds to the regularization term where the norm of the weight vector (w) is calculated. This regularization term is explicitly referred to as the famous L2 norm or Weight Decay. The net result of this is that the weight matrices of the network are penalized according to the value of the regularization parameter lambda (λ). So, the regularization parameter _λ _can be thought of as another hyper-parameter that is required to fine-tune.
The intuition behind the regularizing impact of the L2 norm can be understood by taking an extreme case. Let’s set the value of the regularization parameter _λ _to be on the higher end. This would heavily penalize the weight matrices (w) of the network and they will be assigned with the values that are near to zero. The immediate impact of this is that the net activations of the neural network are reduced and the forward pass effect is diminished. Now, with a much simplified neural network architecture, the model would not be able to overfit to the training data and will be able to generalize much better on novel data and features.
Analysis of regularized vs unregularized networks
This intuition was based on the fact that the value of the regularization parameter _λ _was set to be very high. If however, an intermediate value of this parameter is chosen, it would increase the model performance on the testing set.
#machine-learning #neural-networks #regularization #deep-learning #artificial-intelligence
This cheat sheet helps you to choose the proper estimate for the task that is the hardest portion of the work. With modern computer technology, today’s machine learning isn’t like machine learning from the past.
The notion that computer may learn without being trained to do certain tasks came from pattern recognition researchers interested in artificial intelligence sought to explore if computers could learn from the information.
The iterative component of machine education is crucial because they may adjust autonomously when models are exposed to fresh data. From past calculations, they learn to create dependable, repeatable judgments and results. It’s not a new science, but a new one.
The usage of programming and even equipment is automation for computerized commands. AI, again, is the robots’ ability to reproduce human habits and thinking and get more clever all the time. It is important, while a misleadingly sharp computer may learn and modify its job as it receives new information, it cannot completely replace people. Everything is equal, it’s a resource, not a risk.
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