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).

The key challenge in semi-supervised learning is how to effectively leverage unlabeled data to improve learning performance. The classical label propagation method, despite its popularity, has limited modeling capability in that it only exploits graph information for making predictions. In this paper, we consider label propagation from a graph signal processing perspective and decompose it into three components: signal, filter, and classifier. By extending the three components, we propose a simple generalized label propagation (GLP) framework for semi-supervised learning. GLP naturally integrates graph and data feature information, and offers the flexibility of selecting appropriate filters and domain-specific classifiers for different applications. Interestingly, GLP also provides new insight into the popular graph convolutional network and elucidates its working mechanisms. Extensive experiments on three citation networks, one knowledge graph, and one image dataset demonstrate the efficiency and effectiveness of GLP.

We propose and analyze a method for semi-supervised learning from partially-labeled network-structured data. Our approach is based on a graph signal recovery interpretation under a clustering hypothesis that labels of data points belonging to the same well-connected subset (cluster) are similar valued. This lends naturally to learning the labels by total variation (TV) minimization, which we solve by applying a recently proposed primal-dual method for non-smooth convex optimization. The resulting algorithm allows for a highly scalable implementation using message passing over the underlying empirical graph, which renders the algorithm suitable for big data applications. By applying tools of compressed sensing, we derive a sufficient condition on the underlying network structure such that TV minimization recovers clusters in the empirical graph of the data. In particular, we show that the proposed primal-dual method amounts to maximizing network flows over the empirical graph of the dataset. Moreover, the learning accuracy of the proposed algorithm is linked to the set of network flows between data points having known labels. The effectiveness and scalability of our approach is verified by numerical experiments.

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

As machine learning transitions increasingly towards real world applications controlling the test-time cost of algorithms becomes more and more crucial. Recent work, such as the Greedy Miser and Speedboost, incorporate test-time budget constraints into the training procedure and learn classifiers that provably stay within budget (in expectation). However, so far, these algorithms are limited to the supervised learning scenario where sufficient amounts of labeled data are available. In this paper we investigate the common scenario where labeled data is scarce but unlabeled data is available in abundance. We propose an algorithm that leverages the unlabeled data (through Laplace smoothing) and learns classifiers with budget constraints. Our model, based on gradient boosted regression trees (GBRT), is, to our knowledge, the first algorithm for semi-supervised budgeted learning.

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].

We introduce $\textit{semi-unsupervised learning}$, an extreme case of semi-supervised learning with ultra-sparse categorisation where some classes have no labels in the training set. That is, in the training data some classes are sparsely labelled and other classes appear only as unlabelled data. Many real-world datasets are conceivably of this type. We demonstrate that effective learning in this regime is only possible when a model is capable of capturing both semi-supervised and unsupervised learning. We develop two deep generative models for classification in this regime that extend previous deep generative models designed for semi-supervised learning. By changing their probabilistic structure to contain a mixture of Gaussians in their continuous latent space, these new models can learn in both unsupervised and semi-unsupervised paradigms. We demonstrate their performance both for semi-unsupervised and unsupervised learning on various standard datasets. We show that our models can learn in an semi-unsupervised manner on Fashion-MNIST. Here we artificially mask out all labels for half of the classes of data and keep $2\%$ of labels for the remaining classes. Our model is able to learn effectively, obtaining a trained classifier with $(77.2\pm1.3)\%$ test set accuracy. We also can train on Fashion-MNIST unsupervised, obtaining $(75.2\pm1.5)\%$ test set accuracy. Additionally, doing the same for MNIST unsupervised we get $(96.3\pm0.9)\%$ test set accuracy, which is state-of-the art for fully probabilistic deep generative models.

Systematic trading strategies are algorithmic procedures that allocate assets aiming to optimize a certain performance criterion. To obtain an edge in a highly competitive environment, the analyst needs to proper fine-tune its strategy, or discover how to combine weak signals in novel alpha creating manners. Both aspects, namely fine-tuning and combination, have been extensively researched using several methods, but emerging techniques such as Generative Adversarial Networks can have an impact into such aspects. Therefore, our work proposes the use of Conditional Generative Adversarial Networks (cGANs) for trading strategies calibration and aggregation. To this purpose, we provide a full methodology on: (i) the training and selection of a cGAN for time series data; (ii) how each sample is used for strategies calibration; and (iii) how all generated samples can be used for ensemble modelling. To provide evidence that our approach is well grounded, we have designed an experiment with multiple trading strategies, encompassing 579 assets. We compared cGAN with an ensemble scheme and model validation methods, both suited for time series. Our results suggest that cGANs are a suitable alternative for strategies calibration and combination, providing outperformance when the traditional techniques fail to generate any alpha.

Researchers at the Korea Advanced Institute of Science and Technology and Pohang University of Science and Technology have introduced a machine learning algorithm system, InstaGAN, which can perform multiple instance-aware image-to-image translation tasks -- such as replacing sheep in photos with giraffes -- on multiple image datasets. The paper InstaGAN: Instance-Aware Image-to-Image Translation has been accepted by the respected International Conference on Learning Representations (ICLR) 2019, which will take place this May in New Orleans, USA. An image-to-image translation system is a system that learns to map an input image onto an output image. Unsupervised image-to-image translation has garnered considerable research attention recently in part due to the rapid development of generative adversarial networks (GANs) that now power the technique. Previous methods were not suitable for challenging tasks, for example if the image has multiple target instances or if the translation task involves challenging shapes.

We quantify the separation between the numbers of labeled examples required to learn in two settings: Settings with and without the knowledge of the distribution of the unlabeled data. More specifically, we prove a separation by $\Theta(\log n)$ multiplicative factor for the class of projections over the Boolean hypercube of dimension $n$. We prove that there is no separation for the class of all functions on domain of any size. Learning with the knowledge of the distribution (a.k.a. fixed-distribution learning) can be viewed as an idealized scenario of semi-supervised learning where the number of unlabeled data points is so great that the unlabeled distribution is known exactly. For this reason, we call the separation the value of unlabeled data.