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

Synthetic data offers the promise of cheap and bountiful training data for settings where labeled real-world data is scarce. However, models trained on synthetic data significantly underperform when evaluated 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 perturbs the amplitude spectra of synthetic images in the Fourier domain to generate augmented views. Specifically, with Pasta we propose a structured perturbation strategy where high-frequency components are perturbed relatively more than the low-frequency ones. For the tasks of semantic segmentation (GTAV→Real), object detection (Sim10K→Real), and object recognition (VisDA-C Syn→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.