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)
A6 months old baby won't even notice if a toy truck drives off a platform and seems to fly in the air. However, if the same experiment is repeated 2 to 3 months later, the baby will immediately identify that something is wrong. This means that the baby has already learned the concept of gravity. "Nobody tells a baby that objects are supposed to fall," said the chief AI scientist at Facebook and a professor at NYU, Dr. Yann LeCun, during a webinar organized by the Association for Computing Machinery, an industry body. Because babies do not have very sophisticated motor control, LeCun hypothesizes, "a lot of what they learn about the world is through observation."
Data is the reason AV companies are racking up miles and miles of testing experience on public roads, recording and stockpiling petabytes of road lore. Waymo, for example, claimed in July more than 10 million miles in the real world and 10 billion miles in simulation. But here's yet another question the industry does not like to ask: Assume that AV companies have already collected petabytes or even exabytes of data on real roads. How much of that dataset has been labeled? Perhaps more important, how accurate is the data that's been annotated?
High-quality textures are an important component in many applications ranging from architectural rendering to game design and often require the work of skilled artists. The synthesis of natural textures is therefore an important problem, but the complexity of texture synthesis increases exponentially with the target size of the output texture. Recent advances in the field of Generative Adversarial Networks allow for novel techniques in this field with unprecedented visual quality. TileGAN tackles the problem of texture synthesis in the setting where many input images are given and a large-scale output is required. We propose an algorithm to combine outputs of GANs to produce high-resolution texture maps with virtually no boundary artifacts.
Taken from: A Style-Based Generator Architecture for Generative Adversarial Networks. We can review each of these changes in more detail. The StyleGAN generator and discriminator models are trained using the progressive growing GAN training method. This means that both models start with small images, in this case, 4 4 images. The models are fit until stable, then both discriminator and generator are expanded to double the width and height (quadruple the area), e.g. 8 8. A new block is added to each model to support the larger image size, which is faded in slowly over training. Once faded-in, the models are again trained until reasonably stable and the process is repeated with ever-larger image sizes until the desired target image size is met, such as 1024 1024.
I was working on creating embeddings for medical concepts. These terms/phrases are used for annotating biomedical documents. Now usually the method of creating a co-occurrence matrix and then factorizing it to obtain dense, lower-dimensional vectors is termed as unsupervised learning since annotated data is not involved. I am using the same process but for the annotations themselves. Does this qualify as supervised learning since I need annotated data or does this qualify as unsupervised learning since the method of obtaining the embeddings is unsupervised?
The results from these contests come in many practical forms. They can be used to enhance service to customers, to create a virtual marketing "influencer", and to create art. Recently, GAN-created art sold at Christie's Inc. auction house for over $400,000!3 On the business front, marketers are turning to synthesized personas to serve as their social media "influencer", since it allows them to more-effectively control messaging to their target audience.4 In the app economy, GANs can be used to enhance facial authentication.
If you ask any group of data science students about the types of machine learning algorithms, they will answer without hesitation: supervised and unsupervised. However, if we ask that same group to list different types of unsupervised learning, we are likely to get an answer like clustering but not much more. While supervised methods lead the current wave of innovation in areas such as deep learning, there is very little doubt that the future of artificial intelligence(AI) will transition towards more unsupervised forms of learning. In recent years, we have seen a lot of progress on several new forms of unsupervised learning methods that expand way beyond traditional clustering or principal component analysis(PCA) techniques. Today, I would like to explore some of the most prominent new schools of thought in the unsupervised space and their role in the future of AI.
In a field like Computer Vision, which has been explored and studied for long, Generative Adversarial Network (GAN) was a recent addition which instantly became a new standard for training machines. GAN is an architecture developed by Ian Goodfellow and his colleagues in 2014 which makes use of multiple neural networks that compete against each other to make better predictions. Generator, the network that is responsible for generating new data from training data, and Discriminator, the one that identifies and distinguishes a generated image/fake image from an original image of the training set together form a GAN. Both these networks learn based on their previous predictions, competing with each other for a better outcome. In this article we will break down a simple GAN made with Keras into 8 simple steps.
Selecting the right set of features to be used for data modelling has been shown to improve the performance of supervised and unsupervised learning, to reduce computational costs such as training time or required resources, in the case of high-dimensional input data to mitigate the curse of dimensionality. Computing and using feature importance scores is also an important step towards model interpret-ability. This post shares the overview of supervised and unsupervised methods for performing feature selection I have acquired after researching the topic for a few days. For all depicted methods I also provide references to open-source python implementations I used in order to allow you to quickly test out the presented algorithms. However, this research domain is very abundant in terms of methods which have been proposed during the last 2 decades and as such this post only attempts to present my current limited view without any pretense for completeness.