MIT researchers claim augmentation technique can train GANs with less data - Earth News Report
Researchers at MIT, Adobe Research, and Tsinghua University say they’ve developed a method — differentiable augmentation (DiffAugment) — that improves the efficiency of generative adversarial networks (GANs) by augmenting both real and fake data samples. In a preprint paper, they claim it effectively stabilizes the networks during training, enabling them to generate high-fidelity images using
