GANs Settle Scores!

no code implementations • 2 Jun 2023 • Siddarth Asokan, Nishanth Shetty, Aadithya Srikanth, Chandra Sekhar Seelamantula

Generative adversarial networks (GANs) comprise a generator, trained to learn the underlying distribution of the desired data, and a discriminator, trained to distinguish real samples from those output by the generator.

Data Interpolants -- That's What Discriminators in Higher-order Gradient-regularized GANs Are

no code implementations • 1 Jun 2023 • Siddarth Asokan, Chandra Sekhar Seelamantula

We show analytically, via the least-squares (LSGAN) and Wasserstein (WGAN) GAN variants, that the discriminator optimization problem is one of interpolation in $n$-dimensions.

Decoder

Spider GAN: Leveraging Friendly Neighbors to Accelerate GAN Training

2 code implementations • CVPR 2023 • Siddarth Asokan, Chandra Sekhar Seelamantula

We demonstrate the efficacy of the Spider approach on DCGAN, conditional GAN, PGGAN, StyleGAN2 and StyleGAN3.

Transfer Learning

Teaching a GAN What Not to Learn

1 code implementation • NeurIPS 2020 • Siddarth Asokan, Chandra Sekhar Seelamantula

Generative adversarial networks (GANs) were originally envisioned as unsupervised generative models that learn to follow a target distribution.

Philosophy