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Synthetic data offers the promise of cheap and bountiful training data forsettings where labeled real-world data is scarce. However, models trained onsynthetic data significantly underperform when evaluated on real-world data. Inthis paper, we propose Proportional Amplitude Spectrum Training Augmentation(PASTA), a simple and effective augmentation strategy to improve out-of-the-boxsynthetic-to-real (syn-to-real) generalization performance. PASTA perturbs theamplitude spectra of synthetic images in the Fourier domain to generateaugmented views. Specifically, with PASTA we propose a structured perturbationstrategy where high-frequency components are perturbed relatively more than thelow-frequency ones. For the tasks of semantic segmentation (GTAV-to-Real),object detection (Sim10K-to-Real), and object recognition (VisDA-CSyn-to-Real), across a total of 5 syn-to-real shifts, we find that PASTAoutperforms more complex state-of-the-art generalization methods while beingcomplementary to the same.

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