ODE

Blob Pitt's next big blockbuster Generative models have reached a remarkable capacity to synthesize original instances after learning a data distribution. In the arena of image generation, the recent SOTA tracks alongside advances in a family of models called generative adversarial networks or GANs. This framework of jointly training two networks gives rise to a learnable loss. Despite these successes, GANs are challenged by training instabilities. The latest StyleGAN2-ada mitigates mode collapse arising from overfit discriminators using test time data augmentation....