This paper addresses the ability of generative adversarial networks (GANs) to model complex distributions of data in high-dimensional spaces. Our proposition is that the more effective the adversary is in discriminating the output of the generator, the more effective the generator will be at modeling (or generating) the distribution represented by the training data. The most extreme failure of GANs in this context is mode collapse, and there are several proposed methods to address that problem. However, mode collapse is merely a symptom of a more general problem of GANs, where the generator fools the adversary while failing to faithfully model the distribution of the training data. Here, we address the challenge of constructing and evaluating GANs that more effectively represent the input distribution. We introduce an adversarial architecture that processes sets of generated and real samples, and discriminates between the origins of these sets (i.e., training versus generated data) in a flexible, permutation invariant manner. We present quantitative and qualitative results that demonstrate the effectiveness of this approach relative to state-of-the-art methods for avoiding mode collapse.
@cite_0: We introduce a method to stabilize Generative Adversarial Networks (GANs) by defining the generator objective with respect to an unrolled optimization of the discriminator. This allows training to be adjusted between using the optimal discriminator in the generator's objective, which is ideal but infeasible in practice, and using the current value of the discriminator, which is often unstable and leads to poor solutions. We show how this technique solves the common problem of mode collapse, stabilizes training of GANs with complex recurrent generators, and increases diversity and coverage of the data distribution by the generator.
@cite_18: We propose a new framework for estimating generative models via an adversarial process, in which we simultaneously train two models: a generative model G that captures the data distribution, and a discriminative model D that estimates the probability that a sample came from the training data rather than G. The training procedure for G is to maximize the probability of D making a mistake. This framework corresponds to a minimax two-player game. In the space of arbitrary functions G and D, a unique solution exists, with G recovering the training data distribution and D equal to ½ everywhere. In the case where G and D are defined by multilayer perceptrons, the entire system can be trained with backpropagation. There is no need for any Markov chains or unrolled approximate inference networks during either training or generation of samples. Experiments demonstrate the potential of the framework through qualitative and quantitative evaluation of the generated samples.
In recent years, several training strategies have been proposed to mitigate model collapse in GANs. Metz @cite_0 , for instance, modified the training strategy, such that at each gradient update step @math , the generator's update is computed based on the discriminator's state at step @math to match those that the optimal discriminator would allow. This strategy stabilizes GAN training and decreases mode collapse, but it significantly increases the computational complexity of each update step, as the discriminator calculates derivatives @math times more than the vanilla GAN. Other strategies modify the GAN's loss function and network architecture. For instance, Zhao @cite_18 proposed a nonstochastic alternative loss and an autoencoder-like architecture for the discriminator to learn the manifold of the training data.