Synthetic data offers the promise of cheap and bountiful training data for settings where lots of labeled real-world data for tasks is unavailable. However, models trained on synthetic data significantly underperform on real-world data. In this paper, we propose Proportional Amplitude Spectrum Training Augmentation (PASTA), a simple and effective augmentation strategy to improve out-of-the-box synthetic-to-real (syn-to-real) generalization performance. PASTA involves perturbing the amplitude spectrums of the synthetic images in the Fourier domain to generate augmented views. We design PASTA to perturb the amplitude spectrums in a structured manner such that high-frequency components are perturbed relatively more than the low-frequency ones. For the tasks of semantic segmentation (GTAV to Real), object detection (Sim10K to Real), and object recognition (VisDA-C Syn to Real), across a total of 5 syn-to-real shifts, we find that PASTA outperforms more complex state-of-the-art generalization methods while being complementary to the same.

本文提出PASTA技术通过改变合成数据在傅里叶域中的频谱振幅，在纹理级别上产生增强的视角，这使得它在五个syn-to-real任务中超越更复杂的现有方法且互补性很强。

PASTA: Proportional Amplitude Spectrum Training Augmentation for Syn-to-Real Domain Generalization