High-Fidelity Image Generation With Fewer Labels
This reduces labeling costs for generative models in computer vision, though it is incremental over existing conditional GAN methods.
The paper tackles the problem of high-fidelity image generation requiring large labeled datasets by leveraging self- and semi-supervised learning, achieving state-of-the-art FID scores on ImageNet with only 10-20% of labels.
Deep generative models are becoming a cornerstone of modern machine learning. Recent work on conditional generative adversarial networks has shown that learning complex, high-dimensional distributions over natural images is within reach. While the latest models are able to generate high-fidelity, diverse natural images at high resolution, they rely on a vast quantity of labeled data. In this work we demonstrate how one can benefit from recent work on self- and semi-supervised learning to outperform the state of the art on both unsupervised ImageNet synthesis, as well as in the conditional setting. In particular, the proposed approach is able to match the sample quality (as measured by FID) of the current state-of-the-art conditional model BigGAN on ImageNet using only 10% of the labels and outperform it using 20% of the labels.