Unsupervised or Indirectly Supervised Learning

Unsupervised learning may sound like a fancy way to say "let the kids learn on their own not to touch the hot oven" but it's actually a pattern-finding technique for mining inspiration from your data. It has nothing to do with machines running around without adult supervision, forming their own opinions about things. This post is beginner-friendly, but assumes you're familiar with the story so far: Check out the six instances above. These photographs are not accompanied by labels. No worries, your brain is pretty good at unsupervised learning.

With digital video content creation going viral and assuming the bulk of Internet traffic, how can the deluge of video content be analyzed effectively to derive insights and ROI? After all, video is not only huge in size, but it is complex given various visual, audio and temporal elements. Video summarization (a mechanism for generating a short video summary via key frame analysis or video skimming) has become a popular research topic industry-wide and across academia. Video thumbnail generation and summarization has been developed for years, but deep learning and reinforcement learning is changing the landscape and emerging as the winner for optimal frame selection. Recent advances in Generative Adversarial Networks (GANs) are improving the quality, aesthetics and relevancy of the frames to represent the original videos.

Now we have the probability that each data point belongs to each cluster. If we need hard cluster assignments, we can just choose for each data point to belong to the cluster with the highest probability. But the nice thing about EM is that we can embrace the fuzziness of the cluster membership. We can look at a data point and consider the fact that while it most likely belongs to Cluster B, it's also quite likely to belong to Cluster D. This also takes into account the fact that there may not be clear cut boundaries between our clusters. These groups consist of overlapping multi-dimensional distributions, so drawing clear cut lines might not always be the best solution.

Output of a conditional convolutional GAN trained with spectral normalization on light-field/holography pairs. The generator learns the conditional distribution of holograms (grey, left panels) given a certain light field (red colours on the right).

Over the past 10 years, commercial AI has enjoyed what we at Amazon call the flywheel effect: customer interactions with AI systems generate data; with more data, machine learning algorithms perform better, which leads to better customer experiences; better customer experiences drive more usage and engagement, which in turn generate more data. Those data are used to train machine learning systems in three chief ways. The first is supervised learning, in which the training data are hand-labeled (with, say, words' parts of speech or the names of objects in an image) and the system learns to apply labels to unlabeled data. A variation of this is weakly supervised learning, which uses easily acquired but imprecise labels to enable machine learning at scale. If a website visitor performs a search, for instance, the links she clicks indicate which search results should have been at the top of the list; that kind of implicit information can be used to automatically label data.

We present label gradient alignment, a novel algorithm for semi-supervised learning which imputes labels for the unlabeled data and trains on the imputed labels. We define a semantically meaningful distance metric on the input space by mapping a point (x, y) to the gradient of the model at (x, y). We then formulate an optimization problem whose objective is to minimize the distance between the labeled and the unlabeled data in this space, and we solve it by gradient descent on the imputed labels. We evaluate label gradient alignment using the standardized architecture introduced by Oliver et al. (2018) and demonstrate state-of-the-art accuracy in semi-supervised CIFAR-10 classification.

Benchmark data sets are of vital importance in machine learning research, as indicated by the number of repositories that exist to make them publicly available. Although many of these are usable in the stream mining context as well, it is less obvious which data sets can be used to evaluate data stream clustering algorithms. We note that the classic Covertype data set's size makes it attractive for use in stream mining but unfortunately it is specifically designed for classification. Here we detail the process of transforming the Covertype data set into one amenable for unsupervised learning, which we call the Wilderness Area data set. Our quantitative analysis allows us to conclude that the Wilderness Area data set is more appropriate for unsupervised learning than the original Covertype data set.

The ability of generative adversarial networks to render nearly photorealistic images leads us to ask: What does a GAN know? For example, when a GAN generates a door on a building but not in a tree (Figure 1a), we wish to understand whether such structure emerges as pure pixel patterns without explicitrepresentation, or if the GAN contains internal variables that correspond to human-perceived objects such as doors, buildings, and trees. And when a GAN generates an unrealistic image (Figure 1f), we want to know if the mistake is caused by specific variables in the network. We present a method for visualizing and understanding GANs at different levels of abstraction, from each neuron, to each object, to the relationship between different objects. Beginning witha Progressive GAN (Karras et al., 2018) trained to generate scenes (Figure 1b), we first identify a group of interpretable units that are related to semantic classes (Figure 1a,Figure 2). These units' featuremaps closely match the semantic segmentation of a particular object class (e.g., doors). Then, we directly intervene within the network to identify sets of units that cause a type of object to disappear (Figure1c) or appear (Figure 1d). Finally, we study contextual relationships by observing where we can insert the object concepts in new images and how this intervention interacts with other objects in the image (Figure 1d, Figure 8). This framework enables several applications: comparing internal representationsacross different layers, GAN variants, and datasets (Figure 2); debugging and improving GANs by locating and ablating artifact-causing units (Figure 1e,f,g); understanding contextual relationships between objects in natural scenes (Figure 8,Figure 9); and manipulating images with interactive object-level control (video).

Generative Adversarial Networks (GANs) are a powerful type of neural network used for unsupervised machine learning. They are incredibly important in the context of modern artificial intelligence. In this video, we take a look at what GANs are and how they work. Find us on Facebook -- http://www.facebook.com/PacktPub Follow us on Twitter - http://www.twitter.com/packtpub

This post discusses semi-supervised learning algorithms that learn from proxy labels assigned to unlabelled data. Note: Parts of this post are based on my ACL 2018 paper Strong Baselines for Neural Semi-supervised Learning under Domain Shift with Barbara Plank. Unsupervised learning constitutes one of the main challenges for current machine learning models and one of the key elements that is missing for general artificial intelligence. While unsupervised learning on its own is still elusive, researchers have a made a lot of progress in combining unsupervised learning with supervised learning. This branch of machine learning research is called semi-supervised learning. Semi-supervised learning has a long history. For a (slightly outdated) overview, refer to Zhu (2005) [1] and Chapelle et al. (2006) [2].