Generative Adversarial Networks (GANs) produce high-quality images but are challenging to train. They need careful regularization, vast amounts of compute, and expensive hyper-parameter sweeps. We make significant headway on these issues by projecting generated and real samples into a fixed, pretrained feature space. Motivated by the finding that the discriminator cannot fully exploit features from deeper layers of the pretrained model, we propose a more effective strategy that mixes features across channels and resolutions. Our Projected GAN improves image quality, sample efficiency, and convergence speed. It is further compatible with resolutions of up to one Megapixel and advances the state-of-the-art Fr\'echet Inception Distance (FID) on twenty-two benchmark datasets. Importantly, Projected GANs match the previously lowest FIDs up to 40 times faster, cutting the wall-clock time from 5 days to less than 3 hours given the same computational resources.

本文提出了一种基于预训练特征空间的投影生成对抗网络（GAN）来改善GAN的训练难度，通过将所生成的和真实样本投影到该空间中，激活深层特征，并混合深层特征中的通道和分辨率，从而提高图像质量和训练速度，并成功地将Frechet Inception Distance （FID）值在22个基准数据集上进一步提高，同时加快了训练速度。

投影 GAN 更快收敛