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Ishaan Gulrajani

Alumni

Publications

Improved Training of Wasserstein GANs
Ishaan Gulrajani
Faruk Ahmed
Martin Arjovsky
Vincent Dumoulin
Aaron Courville
Generative Adversarial Networks (GANs) are powerful generative models, but suffer from training instability. The recently proposed Wasserste… (voir plus)in GAN (WGAN) makes progress toward stable training of GANs, but sometimes can still generate only low-quality 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 over discrete data. We also achieve high quality generations on CIFAR-10 and LSUN bedrooms.
2017-03-31
ArXiv (prépublication)
arxiv.org
arxiv.org
Improved Training of Wasserstein GANs
Ishaan Gulrajani
Faruk Ahmed
Martin Arjovsky
Vincent Dumoulin
Aaron Courville
Generative Adversarial Networks (GANs) are powerful generative models, but suffer from training instability. The recently proposed Wasserste… (voir plus)in GAN (WGAN) makes progress toward stable training of GANs, but sometimes can still generate only low-quality 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 over discrete data. We also achieve high quality generations on CIFAR-10 and LSUN bedrooms.
arxiv.org
PixelVAE: A Latent Variable Model for Natural Images
Ishaan Gulrajani
Kundan Kumar
Faruk Ahmed
Adrien Ali Taiga
Francesco Visin
David Vazquez
Aaron Courville
Natural image modeling is a landmark challenge of unsupervised learning. Variational Autoencoders (VAEs) learn a useful latent representatio… (voir plus)n and model global structure well but have difficulty capturing small details. PixelCNN models details very well, but lacks a latent code and is difficult to scale for capturing large structures. We present PixelVAE, a VAE model with an autoregressive decoder based on PixelCNN. Our model requires very few expensive autoregressive layers compared to PixelCNN and learns latent codes that are more compressed than a standard VAE while still capturing most non-trivial structure. Finally, we extend our model to a hierarchy of latent variables at different scales. Our model achieves state-of-the-art performance on binarized MNIST, competitive performance on 64 × 64 ImageNet, and high-quality samples on the LSUN bedrooms dataset.
2017-01-01
ICLR.cc/2017/conference (poster)
openreview.net
openreview.net
SampleRNN: An Unconditional End-to-End Neural Audio Generation Model
Soroush Mehri
Kundan Kumar
Ishaan Gulrajani
Rithesh Kumar
Shubham Jain
Jose Sotelo
Aaron Courville
Yoshua Bengio
In this paper we propose a novel model for unconditional audio generation task that generates one audio sample at a time. We show that our m… (voir plus)odel which profits from combining memory-less modules, namely autoregressive multilayer perceptron, and stateful recurrent neural networks in a hierarchical structure is de facto powerful to capture the underlying sources of variations in temporal domain for very long time on three datasets of different nature. Human evaluation on the generated samples indicate that our model is preferred over competing models. We also show how each component of the model contributes to the exhibited performance.
2017-01-01
ICLR.cc/2017/conference (poster)
openreview.net
openreview.net