Unsupervised learning is a branch of machine learning that learns from test data that has not been labeled, classified or categorized. Instead of responding to feedback, unsupervised learning identifies commonalities in the data and reacts based on the presence or absence of such commonalities in each new piece of data. (Wikipedia)
Now it's time for the most exciting part of our project, from here on we are going to write our code for Generative Adversarial Network (GAN). We are going to use Keras -- A Deep Learning Library to create our GAN. Before starting let's briefly understand what is GAN and it's structure. Generative adversarial networks (GANs) are an exciting recent innovation in machine learning. It was first introduced by Ian Godfellow in his paper Generative Adversarial Networks.
If you're a beginner, machine learning can be confusing for you– how to choose which algorithms to use, from the apparently limitless options, and how to know which one will provide the right predictions (data outputs). The machine learning is a way for computers to run various algorithms without direct human oversight in order to learn from data. So, just before starting with Machine learning algorithms, let's have a look at types of Machine learning which clarify these algorithms. Machine learning algorithms are programs that can learn from data and improve from experience, without human interference. Learning tasks may include learning the function that drafts the input to the output, learning the hidden structure in unlabeled data; or'instance-based learning', where a class label is produced for a new instance by analyzing the new instance (row) to instances from the training data, which were stored in memory.
This episode of Fresh from the arXiv is going to be a little different. Normally I skim through all of the AI, computer vision and NLP preprints that came out during the week and pick a few that I consider particularly interesting. Often there is a common theme uniting a few of my choices, but the idea is not really to zoom in on any particular subject. Last week, however, I could not help but fall down a rabbit hole called semi-supervised learning with GANs. I ended up putting together a little introduction into the topic that is not too technical (meaning, it should be understandable to anyone with a vague idea of how a vanilla unsupervised GAN operates), but also provides a few directions to explore in more detail should you be interested.
Clustering is sometimes called "unsupervised classification", a term that I have mixed feelings on for reasons I will cover shortly, but it provides a good enough explanation of the problem to be worth covering. First, the problem is unsupervised -- we won't have a labeled dataset to guide our logic. Secondly we are looking to separate items into classes based on the predictors (technically they are not predictors they are "features" here because there is no response). The difference is that in supervised classification the class structure is known and labeled, whereas in clustering we are inventing the class structure from the feature values alone. In supervised classification we used the labels to single out one class and looked for predictors that had two qualities: 1) They had fairly common values for every example of that class and 2) they separated that class from others.
DeepFakes are created by a deep learning technique known as Generative Adversarial Networks (GANs), where two machine learning models are used to make the counterfeits more believable. By studying the images and videos of a person, in the form of training data, the first model creates a video, while the second model attempts to detect its flaws. These two models work hand-in-hand until they create a video that is believable. DeepFake opens up a whole new world when it comes to unsupervised learning, which is a sub-field of machine learning where machines can learn to teach themselves, and it has been argued to hold great promise when it comes to self-driving vehicles' to detect and recognize obstacles on the road and virtual assistants such as Siri, Cortana and Alexa learning to be more conversational. The real question is, what potential does it have of being misused, like any other technology.
Despite the significant advances in recent years, Generative Adversarial Networks (GANs) are still notoriously hard to train. In this paper, we propose three novel curriculum learning strategies for training GANs. All strategies are first based on ranking the training images by their difficulty scores, which are estimated by a state-of-the-art image difficulty predictor. Our first strategy is to divide images into gradually more difficult batches. Our second strategy introduces a novel curriculum loss function for the discriminator that takes into account the difficulty scores of the real images.
This paper presents a study of semi-supervised learning with large convolutional networks. We propose a pipeline, based on a teacher/student paradigm, that leverages a large collection of unlabelled images (up to 1 billion)... Our main goal is to improve the performance for a given target architecture, like ResNet-50 or ResNext. We provide an extensive analysis of the success factors of our approach, which leads us to formulate some recommendations to produce high-accuracy models for image classification with semi-supervised learning. As a result, our approach brings important gains to standard architectures for image, video and fine-grained classification. For instance, by leveraging one billion unlabelled images, our learned vanilla ResNet-50 achieves 81.2% top-1 accuracy on the ImageNet benchmark.
Consider the problem of splitting photos into one of two categories: cat and dog. First, imagine you are taking a supervised approach to this problem. With a supervised learning algorithm, the agent will be given photos of various dogs and cats as well as labels for each image. The labels will either be "cat" or "dog". As the agent trains, it will learn what features distinguish dogs from cats.
Accurate image and video classification is important for a wide range of computer vision applications, from identifying harmful content, to making products more accessible to the visually impaired, to helping people more easily buy and sell things on products like Marketplace. Facebook AI is developing alternative ways to train our AI systems so that we can do more with less labeled training data overall, and also deliver accurate results even when large, high-quality labeled data sets are simply not available. Today, we are sharing details on a versatile new model training technique that delivers state-of-the-art accuracy for image and video classification systems. This approach, which we call semi-weak supervision, is a new way to combine the merits of two different training methods: semi-supervised learning and weakly supervised learning. It opens the door the door to creating more accurate, efficient production classification models by using a teacher-student model training paradigm and billion-scale weakly supervised data sets.
In this work we present an adversarial training algorithm that exploits correlations in video to learn --without supervision-- an image generator model with a disentangled latent space. The proposed methodology requires only a few modifications to the standard algorithm of Generative Adversarial Networks (GAN) and involves training with sets of frames taken from short videos. We train our model over two datasets of face-centered videos which present different people speaking or moving the head: VidTIMIT and YouTube Faces datasets. We found that our proposal allows us to split the generator latent space into two subspaces. One of them controls content attributes, those that do not change along short video sequences. For the considered datasets, this is the identity of the generated face. The other subspace controls motion attributes, those attributes that are observed to change along short videos. We observed that these motion attributes are face expressions, head orientation, lips and eyes movement. The presented experiments provide quantitative and qualitative evidence supporting that the proposed methodology induces a disentangling of this two kinds of attributes in the latent space.