augmax

**AugMax** is a **data augmentation strategy that adversarially combines multiple augmentation chains to create the most challenging augmented sample** — finding the worst-case mixture of augmentations that maximally increases the training loss, providing robustness training. **How Does AugMax Work?** - **Multiple Chains**: Apply $K$ different augmentation chains to the same input (e.g., $K = 3$). - **Adversarial Mixture**: Find the convex combination $sum_k w_k cdot ext{Aug}_k(x)$ that maximizes the loss. - **Train**: Train the model on this worst-case augmented sample. - **Paper**: Wang et al. (2021). **Why It Matters** - **Adversarial Augmentation**: Goes beyond random augmentation by actively finding the hardest combination. - **Robustness**: Improves both clean accuracy and corruption robustness (ImageNet-C, ImageNet-P). - **Principled**: The adversarial mixture is a principled way to explore the augmentation space efficiently. **AugMax** is **augmentation as an adversary** — finding the hardest possible augmentation mixture to create maximally challenging training samples.