The Generative Adversarial Networks (GAN) is, in fact, never a single network. It is a set of networks, at least two, operating at the same place but working against each other. Each of the networks brings its own unique set of results. For instance, in GAN approach, the first network creates realistic images, while the second one identifies whether those are real or not. It is like the first network is synthesizing something and the second one is monitoring its operations and controls what it creates.

The year 2017 was a period of scientific breakthroughs in deep learning, with the publication of numerous research papers. Every year seems like a big leap toward artificial general intelligence, or AGI. One exciting development involves generative modelling and the use of Wasserstein GANs (Generative Adversarial Networks). An influential paper on the topic has completely changed the approach to generative modelling, moving beyond the time when Ian Goodfellow published the original GAN paper. This paper differs from earlier work: the training algorithm is backed up by theory, and few examples exist where theory-justified papers gave good empirical results.

In this paper, we introduce an unsupervised learning approach to automatically discover, summarize, and manipulate artistic styles from large collections of paintings. Our method is based on archetypal analysis, which is an unsupervised learning technique akin to sparse coding with a geometric interpretation. When applied to deep image representations from a collection of artworks, it learns a dictionary of archetypal styles, which can be easily visualized. After training the model, the style of a new image, which is characterized by local statistics of deep visual features, is approximated by a sparse convex combination of archetypes. This enables us to interpret which archetypal styles are present in the input image, and in which proportion. Finally, our approach allows us to manipulate the coefficients of the latent archetypal decomposition, and achieve various special effects such as style enhancement, transfer, and interpolation between multiple archetypes.

Which authors of this paper are endorsers? Disable MathJax (What is MathJax?)

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]. Particularly recently, semi-supervised learning has seen some success, considerably reducing the error rate on important benchmarks.

We extract losses at two levels, at the end of the generator and at the end of the full model. The first one is a perceptual loss computed directly on the generator's outputs. This first loss ensures the GAN model is oriented towards a deblurring task. It compares the outputs of the first convolutions of VGG. The second loss is the Wasserstein loss performed on the outputs of the whole model.

Scalings in which the graph Laplacian approaches a differential operator in the large graph limit are used to develop understanding of a number of algorithms for semi-supervised learning; in particular the extension, to this graph setting, of the probit algorithm, level set and kriging methods, are studied. Both optimization and Bayesian approaches are considered, based around a regularizing quadratic form found from an affine transformation of the Laplacian, raised to a, possibly fractional, exponent. Conditions on the parameters defining this quadratic form are identified under which well-defined limiting continuum analogues of the optimization and Bayesian semi-supervised learning problems may be found, thereby shedding light on the design of algorithms in the large graph setting. The large graph limits of the optimization formulations are tackled through $\Gamma$-convergence, using the recently introduced $TL^p$ metric. The small labelling noise limit of the Bayesian formulations are also identified, and contrasted with pre-existing harmonic function approaches to the problem.

This paper introduces transductive consensus network (TCNs), as an extension of a consensus network (CN), for semi-supervised learning. TCN does multi-modal classification based on a few available labels by urging the {\em interpretations} of different modalities to resemble each other. We formulate the multi-modal, semi-supervised learning problem, put forward TCN for multi-modal semi-supervised learning task, and its several variants. To understand the mechanisms of TCN, we formulate the {\em similarity} of the interpretations as the negative relative Jensen-Shannon divergence, and show that a consensus state beneficial for classification desires a stable but not perfect similarity between the interpretations. We show the performances of TCN are better than best benchmark algorithms given only 20 and 80 labeled samples on Bank Marketing and the DementiaBank dataset respectively, and align with their performances given more labeled samples.

We propose regularizing the empirical loss for semi-supervised learning by acting on both the input (data) space, and the weight (parameter) space. We show that the two are not equivalent, and in fact are complementary, one affecting the minimality of the resulting representation, the other insensitivity to nuisance variability. We propose a method to perform such smoothing, which combines known input-space smoothing with a novel weight-space smoothing, based on a min-max (adversarial) optimization. The resulting Adversarial Block Coordinate Descent (ABCD) algorithm performs gradient ascent with a small learning rate for a random subset of the weights, and standard gradient descent on the remaining weights in the same mini-batch. It achieves comparable performance to the state-of-the-art without resorting to heavy data augmentation, using a relatively simple architecture.

GANS are powerful generative models that are able to model the manifold of natural images. We leverage this property to perform manifold regularization by approximating the Laplacian norm using a Monte Carlo approximation that is easily computed with the GAN. When incorporated into the feature-matching GAN of Improved GAN, we achieve state-of-the-art results for GAN-based semi-supervised learning on the CIFAR-10 dataset, with a method that is significantly easier to implement than competing methods.