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:Â
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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Fair Attribute Classification through Latent Space De-biasing
TL;DR: This work uses GANs to generate realistic-looking images, and perturb these images in the underlying latent space to generate training data that is balanced for each protected attribute, and empirically demonstrates that target classifiers trained on the augmented dataset exhibit a number of both quantitative and qualitative benefits.
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On the generalization of GAN image forensics.
TL;DR: This paper proposes to use preprocessed images to train a forensic CNN model and shows that by applying similar image level preprocessing to both real and fake training images, the forensics model is forced to learn more intrinsic features to classify the generated and real face images.
Book ChapterDOI
COCO-FUNIT: Few-Shot Unsupervised Image Translation with a Content Conditioned Style Encoder
TL;DR: A new few-shot image translation model, COCO-FUNIT, is proposed, which computes the style embedding of the example images conditioned on the input image and a new module called the constant style bias, which shows effectiveness in addressing the content loss problem.
Proceedings ArticleDOI
Image-to-image Translation via Hierarchical Style Disentanglement
Xinyang Li,Shengchuan Zhang,Jie Hu,Liujuan Cao,Xiaopeng Hong,Xudong Mao,Feiyue Huang,Yongjian Wu,Rongrong Ji +8 more
TL;DR: Li et al. as discussed by the authors propose Hierarchical Style Disentanglement (HiSD), which disentangles the labels into independent tags, exclusive attributes, and disentangled styles from top to bottom.
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Manifold Mixup: Encouraging Meaningful On-Manifold Interpolation as a Regularizer.
TL;DR: This work proposes Manifold Mixup which encourages the network to produce more reasonable and less confident predictions at points with combinations of attributes not seen in the training set by training on convex combinations of the hidden state representations of data samples.
References
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Posted Content
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Improved Techniques for Training GANs
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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.