论文标题
图像重建和合成的焦点损失
Focal Frequency Loss for Image Reconstruction and Synthesis
论文作者
论文摘要
由于生成模型的发展，图像重建和合成已经取得了显着的进步。尽管如此，实际图像和生成的图像之间仍然存在差距，尤其是在频域中。在这项研究中，我们表明频域中的差异可以进一步改善图像重建和合成质量。我们提出了一种新型的焦距损失，该频率损失使模型可以适应地关注频率组件，而频率组件很难通过减轻易于频率来综合。该目标函数与现有的空间损失相辅相成，这为由于神经网络的固有偏见而导致重要频率信息的丢失提供了极大的阻抗。我们以感知质量和定量性能以改善流行模型（例如VAE，Pix2pix和Spade）的流行模型的多功能性和有效性。我们进一步展示了它在StyleGan2上的潜力。
Image reconstruction and synthesis have witnessed remarkable progress thanks to the development of generative models. Nonetheless, gaps could still exist between the real and generated images, especially in the frequency domain. In this study, we show that narrowing gaps in the frequency domain can ameliorate image reconstruction and synthesis quality further. We propose a novel focal frequency loss, which allows a model to adaptively focus on frequency components that are hard to synthesize by down-weighting the easy ones. This objective function is complementary to existing spatial losses, offering great impedance against the loss of important frequency information due to the inherent bias of neural networks. We demonstrate the versatility and effectiveness of focal frequency loss to improve popular models, such as VAE, pix2pix, and SPADE, in both perceptual quality and quantitative performance. We further show its potential on StyleGAN2.
