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arXiv · 2510.22899

On the Anisotropy of Score-Based Generative Models

Andreas Floros·Seyed-Mohsen Moosavi-Dezfooli·Pier Luigi Dragotti
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Abstract

We investigate the role of network architecture in shaping the inductive biases of modern score-based generative models. To this end, we introduce the Score Anisotropy Directions (SADs), architecture-dependent directions that reveal how different networks preferentially capture data structure. Our analysis suggests that SADs form adaptive bases aligned with the architecture's output geometry, providing a principled way to predict generalization ability in score models prior to training. Through both synthetic data and standard image benchmarks, we demonstrate that SADs reliably capture fine-grained model behavior and correlate with downstream performance, as measured by Wasserstein metrics. Our work offers a new lens for explaining and predicting directional biases of generative models.

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Andreas Floros, Seyed-Mohsen Moosavi-Dezfooli, Pier Luigi Dragotti. 2025-10-27. On the Anisotropy of Score-Based Generative Models. https://arxiv.org/abs/2510.22899

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