Breaking the Geometric Bottleneck: Contrastive Expansion in Asymmetric Cross-Modal Distillation
Knowledge distillation between asymmetric architectures often induces severe geometric constraints on the learned representation space. We investigate dimensional collapse when distilling global Vision Transformers into capacity-constrained, local-receptive-field CNNs (0.5M-8.0M parameters). Using strictly centered SVD and Shannon Entropy Effective Rank, we confirm capacity-agnostic collapse under cosine distillation: a CLIP ViT-B/32 Teacher exhibits Effective Rank 88.68 on CIFAR-10, while all cosine-distilled students collapse to ~17 regardless of parameter count. An auxiliary InfoNCE objective expands this to ~41 dimensions. Critically, we ask whether this expansion is functionally usef...
| Authors | Kabir Thayani |
|---|---|
| Primary category | cs.CV |
| Published | 5 March 2026 |
| Last updated at source | 2 September 2026 |
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