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Insights into Closed-form IPM-GAN Discriminator Guidance for Diffusion Modeling
Aadithya Srikanth, Siddarth Asokan, Nishanth Shetty +1
Diffusion models are a state-of-the-art generative modeling framework that transform noise to images via Langevin sampling, guided by the score, which is the gradient of the logari…
Data Interpolants -- That's What Discriminators in Higher-order Gradient-regularized GANs Are
Siddarth Asokan, Chandra Sekhar Seelamantula
We consider the problem of optimizing the discriminator in generative adversarial networks (GANs) subject to higher-order gradient regularization. We show analytically, via the lea…
Spider GAN: Leveraging Friendly Neighbors to Accelerate GAN Training
Siddarth Asokan, Chandra Sekhar Seelamantula
Training Generative adversarial networks (GANs) stably is a challenging task. The generator in GANs transform noise vectors, typically Gaussian distributed, into realistic data suc…