Oct. 29, 2017
The other day I got a question how to do wavelet transformation in PyTorch in a way that allows to compute gradients (that is gradients of outputs w.r.t. the inputs, probably not the coefficients). I like Pytorch and I happen to have a certain fancy for wavelets as well, so here we go.
May 29, 2017
This is following up on my post on improved and semi-improved training of Wasserstein GANs. A few days ago, Kodaldi et al published How to Train Your DRAGAN. They introduce an algorithmic game theory approach and propose to apply the gradient penalty only close to the real-data manifold. We take a look at their objective function, offer a new possible interpretation and also consider what might be wrong in Improved Training objective.
While doing so we introduce PRODGAN and SLOGAN.
April 13, 2017
We look at Improved Training of Wasserstein GANs and describe some geometric intuition on how it improves over the original Wasserstein GAN article.
Updated: We also introduce Semi-Improved Training of Wasserstein GANs, a variant that is simpler to implement as it does not need second derivatives.