Generative adversarial networks (GANs) have seen widespread adoption in machine learning, especially in computer vision applications. These "generative" models are capable of producing artificial images in many instances indistinguishable from the real thing. The most common use of these networks is that of artificial face generation. These so-called "deepfakes" have been used in a number of research and commercial applications. With their proliferation, however, have come predictable problems of bias in their generation. All such models are trained on large datasets. Several pre-trained models for StyleGANs 2 and 3 are trained on the Flickr (FFHQ) dataset.

We explore a new way to evaluate generative models using insights from evaluation of competitive games between human players. We show experimentally that tournaments between generators and discriminators provide an effective way to evaluate generative models. We introduce two methods for summarizing tournament outcomes: tournament win rate and skill rating. Evaluations are useful in different contexts, including monitoring the progress of a single model as it learns during the training process, and comparing the capabilities of two different fully trained models. We show that a tournament consisting of a single model playing against past and future versions of itself produces a useful measure of training progress. A tournament containing multiple separate models (using different seeds, hyperparameters, and architectures) provides a useful relative comparison between different trained GANs. Tournament-based rating methods are conceptually distinct from numerous previous categories of approaches to evaluation of generative models, and have complementary advantages and disadvantages.

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.

We present an autoencoder that leverages learned representations to better measure similarities in data space. By combining a variational autoencoder with a generative adversarial network we can use learned feature representations in the GAN discriminator as basis for the VAE reconstruction objective. Thereby, we replace element-wise errors with feature-wise errors to better capture the data distribution while offering invariance towards e.g. translation. We apply our method to images of faces and show that it outperforms VAEs with element-wise similarity measures in terms of visual fidelity. Moreover, we show that the method learns an embedding in which high-level abstract visual features (e.g. wearing glasses) can be modified using simple arithmetic.