Open AccessProceedings Article
Improved training of wasserstein GANs
Ishaan Gulrajani,Faruk Ahmed,Martin Arjovsky,Vincent Dumoulin,Aaron Courville +4 more
- Vol. 30, pp 5769-5779
TLDR
The authors proposed to penalize the norm of the gradient of the critic with respect to its input to improve the training stability of Wasserstein GANs and achieve stable training of a wide variety of GAN architectures with almost no hyperparameter tuning.Abstract:
Generative Adversarial Networks (GANs) are powerful generative models, but suffer from training instability. The recently proposed Wasserstein GAN (WGAN) makes progress toward stable training of GANs, but sometimes can still generate only poor samples or fail to converge. We find that these problems are often due to the use of weight clipping in WGAN to enforce a Lipschitz constraint on the critic, which can lead to undesired behavior. We propose an alternative to clipping weights: penalize the norm of gradient of the critic with respect to its input. Our proposed method performs better than standard WGAN and enables stable training of a wide variety of GAN architectures with almost no hyperparameter tuning, including 101-layer ResNets and language models with continuous generators. We also achieve high quality generations on CIFAR-10 and LSUN bedrooms.read more
Citations
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A Unifying Review of Deep and Shallow Anomaly Detection
Lukas Ruff,Jacob R. Kauffmann,Robert A. Vandermeulen,Grégoire Montavon,Wojciech Samek,Marius Kloft,Thomas G. Dietterich,Klaus-Robert Müller +7 more
TL;DR: This review aims to identify the common underlying principles and the assumptions that are often made implicitly by various methods in deep learning, and draws connections between classic “shallow” and novel deep approaches and shows how this relation might cross-fertilize or extend both directions.
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SEAN: Image Synthesis With Semantic Region-Adaptive Normalization
TL;DR: Semantic Region Adaptive Normalization (SEAN) as mentioned in this paper is a simple but effective building block for Generative Adversarial Networks conditioned on segmentation masks that describe the semantic regions in the desired output image.
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Generative Adversarial Networks for Extreme Learned Image Compression
TL;DR: In this article, an encoder, decoder/generator and a multi-scale discriminator are trained jointly for a generative learned compression objective, obtaining visually pleasing results at bitrates where previous methods fail and show strong artifacts.
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HoloGAN: Unsupervised learning of 3D representations from natural images
TL;DR: HoloGAN is the first generative model that learns 3D representations from natural images in an entirely unsupervised manner and is shown to be able to generate images with similar or higher visual quality than other generative models.
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GAN Dissection: Visualizing and Understanding Generative Adversarial Networks
David Bau,Jun-Yan Zhu,Hendrik Strobelt,Bolei Zhou,Joshua B. Tenenbaum,William T. Freeman,Antonio Torralba +6 more
TL;DR: In this article, the authors identify a group of interpretable units that are closely related to object concepts using a segmentation-based network dissection method and quantitatively quantify the causal effect of these units by measuring the ability of interventions to control objects in the output.
References
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Learning Multiple Layers of Features from Tiny Images
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Journal ArticleDOI
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Posted Content
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Proceedings Article
Categorical Reparameterization with Gumbel-Softmax
Eric Jang,Shixiang Gu,Ben Poole +2 more
TL;DR: Gumbel-Softmax as mentioned in this paper replaces the non-differentiable samples from a categorical distribution with a differentiable sample from a novel Gumbel softmax distribution, which has the essential property that it can be smoothly annealed into the categorical distributions.