How Data Augmentation Shapes Neural Representations
Data augmentation is widely recognized for improving generalization in deep networks, yet its impact on the geometry of learned representations remains poorly understood. In this work, we characterize how different data augmentation strategies reshape internal representations in neural networks. Using tools from shape analysis, we embed network hidden representations into a metric space where distance is invariant to scaling, translation, rotation and reflection. We show that increasing augmentation strength leads to well-behaved trajectories in this space, and that different augmentation types steer representations in distinct directions. Moreover, we investigate how neural representatio...
| Authors | Tianxiao He, Alex H. Williams, Sarah E. Harvey |
|---|---|
| Primary category | cs.LG |
| Published | 14 May 2026 |
| Last updated at source | 14 May 2026 |
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