论文标题
用于数据效率GAN培训的可区分增强
Differentiable Augmentation for Data-Efficient GAN Training
论文作者
论文摘要
如果培训数据有限，生成对抗网络（GAN）的性能严重恶化。这主要是因为歧视者正在记住确切的训练集。为了对抗它，我们提出了可区分的增强（DIFFAUGMENT），这是一种简单的方法，通过对真实和假样品施加各种类型的可区分增强来提高gan的数据效率。以前的尝试直接增强培训数据操纵真实图像的分布，几乎没有收益； Diffaigment使我们能够为生成的样品采用可区分的扩展，从而有效地稳定训练并导致更好的收敛性。实验表明，对于无条件和阶级生成的多种GAN结构和损失函数，我们的方法表现出一致的收益。通过DIFFAUGMENT，我们在ImageNet 128x128上实现了6.80的最新FID，为100.8，而在FFHQ和LSUN上有1,000张图像，FID降低了2-4x。此外，只有20％的培训数据，我们可以匹配CIFAR-10和CIFAR-100的最高性能。最后，我们的方法可以仅使用100张图像生成高保真图像，而无需预训练，同时与现有的转移学习算法相当。代码可在https://github.com/mit-han-lab/data-felpidic-gans上找到。
The performance of generative adversarial networks (GANs) heavily deteriorates given a limited amount of training data. This is mainly because the discriminator is memorizing the exact training set. To combat it, we propose Differentiable Augmentation (DiffAugment), a simple method that improves the data efficiency of GANs by imposing various types of differentiable augmentations on both real and fake samples. Previous attempts to directly augment the training data manipulate the distribution of real images, yielding little benefit; DiffAugment enables us to adopt the differentiable augmentation for the generated samples, effectively stabilizes training, and leads to better convergence. Experiments demonstrate consistent gains of our method over a variety of GAN architectures and loss functions for both unconditional and class-conditional generation. With DiffAugment, we achieve a state-of-the-art FID of 6.80 with an IS of 100.8 on ImageNet 128x128 and 2-4x reductions of FID given 1,000 images on FFHQ and LSUN. Furthermore, with only 20% training data, we can match the top performance on CIFAR-10 and CIFAR-100. Finally, our method can generate high-fidelity images using only 100 images without pre-training, while being on par with existing transfer learning algorithms. Code is available at https://github.com/mit-han-lab/data-efficient-gans.
