Double Descent and Malign Overfitting in Diffusion Models (arxiv.org)

Recent research challenges the conventional understanding of overparameterization in deep learning, particularly in the context of diffusion models. While larger models generally exhibit benign overfitting—leading to better generalization—this study reveals that diffusion models experience "malign overfitting." This occurs as the models enter a memorization regime that negatively impacts performance, which contradicts the expected outcomes of the double-descent phenomenon typically observed in standard regression tasks. Through experiments on U-Nets trained on the CelebA dataset and a theoretical model with closed-form learning curves, the researchers found that while an interpolation peak does occur, it is at a significantly larger parameter-to-sample ratio than in traditional settings. Specifically, the test loss begins to degrade much earlier, around the point where the number of parameters exceeds the number of training samples. This finding underscores the need for regularization techniques, as optimally regularized large models outperformed their unregularized counterparts. The insights from this study are crucial for the AI/ML community as they highlight the importance of careful model training strategies, particularly for diffusion models, to avoid catastrophic overfitting.