DeCAtt: Efficient Vision Transformers with Decorrelated Attention Heads
Bibliographic record
Abstract
The advent of Vision Transformers (ViT) has led to significant performance gains across various computer vision tasks over the last few years, surpassing the de facto standard CNN architectures. However, most of the prominent variations of Vision Transformers are resource-intensive architectures with huge parameter sizes. They are known to be data-hungry and overfit quickly on comparatively smaller datasets. Consequently, this holds back their widespread usage across low-resource settings, which brings forth the need to develop resource-efficient vision transformers. To this end, we introduce a regularization loss that prioritizes efficient utilization of model parameters by decorrelating the heads of a multi-headed attention block in a vision transformer. This forces the heads to learn distinct features rather than focus on the same ones. Using this loss provides a consistent performance improvement over a wide range of varying scenarios of models and datasets as we show in our experiments, which proves its superior effectiveness.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.001 |
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 teacher head, 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".