CCAR: Intrinsic Robustness as an Emergent Geometric Property
Standard supervised learning optimizes for predictive accuracy but remains agnostic to the internal geometry of learned features, often yielding representations that are entangled and brittle. We propose Class-Conditional Activation Regularization (CCAR) to explicitly engineer the feature space, imposing a block-diagonal structure via a soft inductive bias. By shaping the latent representation to confine class energy to orthogonal subspaces, we create an intrinsic geometric scaffold that naturally filters noise and adversarial perturbations. We provide theoretical analysis linking this structural constraint to the maximization of the Fisher Discriminant Ratio, establishing a formal connec...
| Authors | Akash Samanta, Manish Pratap Singh, Debasis Chaudhuri |
|---|---|
| Primary category | cs.LG |
| Published | 18 April 2026 |
| Last updated at source | 18 April 2026 |
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