Smoothed Gradient Descent-Ascent - PyTorch

• This repository contains an implementation of the algorithm presented in the paper "A Single-Loop Smoothed Gradient Descent-Ascent Algorithm for Nonconvex-Concave Min-Max Problems" (NeurIPS 2020)

• This repository applies the algorithm to a Vanilla GAN on MNIST dataset.

• The algorithm roughly translates to using:

  • Stochastic Weight Averaging (SWA) on the discriminator ("slower-moving" discriminator).
  • And a penalty on the Discriminator loss that forces the discriminator to not stray too far away from the SWA version of the Discriminator.

• The above algorithm, Smoothed Gradient Descent-Ascent (SGDA) is implemented at SGDA.py

• This repository also contains an extension of the SGDA algorithm which passes gradients from both Discriminators to the Generator.

• This change in the algorithm roughly translates to:

  • Minimizing Log-likelihood w.r.t to the SWA version of the Discriminator (in addition to the "faster-moving" discriminator)

• The above algorithm, "Double" Smoothed Gradient Descent-Ascent is implemented at DoubleSmoothedGDA.py

Results

DCGAN - MNIST

Latent Space Interpolation.

• Notice Vanilla GDA and SGDA both suffer from Mode Collapse.
• Double Smoothed GDA leads to a very rich latent space.
• To reach this level of Mutual Information (MI) between Latent vector and Generated Images, usually InfoMax like additional penalty term is needed.

Samples

• Again, SGDA suffers from Mode Collapse, while in GDA the Discriminator is fooled too easily by the Generator, hence the poorer results.
• To solve both problems, Wasserstein version of the GAN coupled with InfoMax might be needed.
• Double SGDA doesn't need anything special to avoid Mode Collapse.

Loss Curves

• NOTE: Loss magnitudes are not comparable since all three methods use different loss functions.
• NOTE: These curves are only meant to intrepret the stability of losses of Generator and Discriminator.

To-do

• [ ] Experiments on CelebA

Shameless Plug

The experiments were conducted using the tool "Poor Man's Job Scheduler". TLDR:

• pmrun "python3 foo.py"
• Run Asynchronous jobs on remote/local server as if they were your local machine.
• Without directly messing with SSH.
• Ignore files to sync with a .gitignore-style file.