Closest Neighbors are Harmful for Lightweight Masked Auto-encoders
Bibliographic record
Abstract
Learning the visual representation via masked auto-encoder (MAE) training has been proven to be a powerful technique. Transferring the pre-trained vision transformer (ViT) to downstream tasks leads to superior performance compared to conventional task-by-task supervised learning. Recent research works on MAE focus on large-sized vision transformers (>50 million parameters) with outstanding performance. However, improving the generality of the under-parametrized lightweight model has been widely ignored. In practice, downstream applications are commonly intended for resource-constrained platforms, where large-scale ViT cannot easily meet the resource budget. Current lightweight MAE training heavily relies on knowledge distillation with a pre-trained teacher, whereas the root cause behind the poor performance remains under-explored. Motivated by that, this paper first introduces the concept of "closest neighbor patch" to characterize the local semantics among the input tokens. Our discovery shows that the lightweight model failed to distinguish different local information, leading to aliased understanding and poor accuracy. Motivated by this finding, we propose NoR-MAE, a novel MAE training algorithm for lightweight vision transformers. NoR-MAE elegantly repels the semantic aliasing between patches and their closest neighboring patch (semantic centroid) with negligible training cost overhead. With the ViT-Tiny model, NoR-MAE achieves up to 7.22%/3.64% accuracy improvements on ImageNet-100/ImageNet-1K datasets, as well as up to 5.13% accuracy improvements in tested downstream tasks. https://github.com/SeoLabCornell/NoR-MAE
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 0.003 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".