Title: Proportional Amplitude Spectrum Training Augmentation for Synthetic-to-Real Domain Generalization. Abstract: Synthetic data offers the promise of cheap and bountiful training data for settings where lots of labeled real-world data for some task 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→Real), object detection (Sim10K→Real), and object recognition (VisDAC Syn→Real), across a total of 5 syn-to-real shifts, we find that PASTA  either outperforms or is consistently competitive with more complex state-of-the-art methods while being complementary to other generalization approaches.