Prescribed Generative Adversarial Networks

By Adji Bousso Dieng, Francisco J. R. Ruiz, David M. Blei, Michalis K. Titsias. 2019

61 citations recorded by the source corpus.

Generative adversarial networks (GANs) are a powerful approach to unsupervised learning. They have achieved state-of-the-art performance in the image domain. However, GANs are limited in two ways. They often learn distributions with low support---a phenomenon known as mode collapse---and they do not guarantee the existence of a probability density, which makes evaluating generalization using predictive log-likelihood impossible. In this paper, we develop the prescribed GAN (PresGAN) to address these shortcomings. PresGANs add noise to the output of a density network and optimize an entropy-regularized adversarial loss. The added noise renders tractable approximations of the predictive log-likelihood and stabilizes the training procedure. The entropy regularizer encourages PresGANs to capture all the modes of the data distribution. Fitting PresGANs involves computing the intractable gradients of the entropy regularization term; PresGANs sidestep this intractability using unbiased stochastic estimates. We evaluate PresGANs on several datasets and found they mitigate mode collapse and generate samples with high perceptual quality. We further found that PresGANs reduce the gap in performance in terms of predictive log-likelihood between traditional GANs and variational autoencoders (VAEs).

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition, Generative Models

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Citations in the corpus

Adversarial score matching and improved sampling for image generation (2020, neutral): “We proposed a hybrid approach between 3-Stacked MNIST Modes (Max 1000) KL DCGAN (Radford et al., 2015b) 99.0 3.40 ALI (Dumoulin et al., 2016) 16.0 5.40 Unrolled GAN (Metz et al., 2016) 48.7 4.32 VEEGAN (Srivastava et al., 2017) 150.0 2.95 PacDCGAN2 (Lin et al., 2017b) 1000.0 0.06 WGAN-GP (Kumar et a”
Tackling the Generative Learning Trilemma with Denoising Diffusion GANs (2021, background): “, 2021) and entropy regularization (Dieng et al., 2019).”
The Vendi Score: A Diversity Evaluation Metric for Machine Learning (2022, methodology): “We compare Presgan (Dieng et al., 2019) and Self-conditioned gan (Liu et al.”