Hierarchical full-attention neural architecture search based on search space compression
Bibliographic record
Abstract
Neural architecture search (NAS) has significantly advanced the automatic design of convolutional neural architectures. However, it is challenging to directly extend existing NAS methods to attention networks because of the uniform structure of the search space and the lack of long-range feature extraction. To address these issues, we construct a hierarchical search space that allows various attention operations to be adopted for different layers of a network. To reduce the complexity of the search, a low-cost search space compression method is proposed to automatically remove the unpromising candidate operations for each layer. Furthermore, we propose a novel search strategy combining a self-supervised search with a supervised one to simultaneously capture long-range and short-range dependencies. To verify the effectiveness of the proposed methods, we conduct extensive experiments on various learning tasks, including image classification , fine-grained image recognition, and zero-shot image retrieval . The empirical results show strong evidence that our method is capable of discovering high-performance full-attention architectures while guaranteeing the required search efficiency.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".