High-Fidelity Image Generation With Fewer Labels

High-Fidelity Image Generation With Fewer 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.

标签更少的高保真图像生成

深度生成模型正在成为现代机器学习的基石。关于条件生成对抗网络的最新研究表明，学习自然图像上复杂的高维分布是可以实现的。.. 尽管最新的模型能够以高分辨率生成高保真，多样的自然图像，但它们依赖于大量的标记数据。在这项工作中，我们演示了如何从最近的自我监督和半监督学习工作中受益，从而在无监督ImageNet综合以及有条件的情况下优于现有技术。特别地，所提出的方法能够仅使用10％的标签匹配ImageNet上当前最先进的条件模型BigGAN的样本质量（通过FID进行测量），而使用20％的标签则胜过它。 （阅读更多）