pith:PI7DO5NW
From Per-Image Low-Rank to Encoding Mismatch: Rethinking Feature Distillation in Vision Transformers
An encoding mismatch from per-image low-rank features and rotating dataset subspaces blocks feature-map distillation for compressing Vision Transformers.
arxiv:2511.15572 v3 · 2025-11-19 · cs.CV
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Claims
We refer to this combined phenomenon as an encoding mismatch. We propose two minimal remedies, Lift or WideLast... On ImageNet-1K, these fixes revive feature KD for ViT compression, improving DeiT-Tiny distilled from CaiT-S24 from 74.86% to 77.53%/78.23% top-1 accuracy.
The assumption that the observed per-image low-rank structure, dataset-level subspace rotations, and token-level spectral energy patterns are the primary and causal reasons for feature-map KD failure in compression, rather than other factors such as optimization dynamics or capacity limits.
The paper identifies an encoding mismatch in ViT feature distillation from per-image compressibility versus dataset subspace rotations and broad spectral energy patterns, proposing Lift and WideLast remedies that improve DeiT-Tiny accuracy from 74.86% to 77.53-78.23% on ImageNet-1K.
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| First computed | 2026-05-27T02:05:08.393777Z |
|---|---|
| Builder | pith-number-builder-2026-05-17-v1 |
| Signature | Pith Ed25519
(pith-v1-2026-05) · public key |
| Schema | pith-number/v1.0 |
Canonical hash
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Canonical record JSON
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