Semantic-DARTS: Elevating Semantic Learning for Mobile Differentiable Architecture Search
Bibliographic record
Abstract
Differentiable architecture search (DARTS) is a prevailing direction in automatic machine learning, but it may suffer from performance collapse and generalization issues. Recent efforts mitigate them by integrating regularization into architectural parameters or rule-based operations selection. These efforts primarily emphasize learning the global class-specific features through the image classification task, while overlooking the fine-grained local information during the search process. In this article, we take the first trial to observe that three semantic challenges arise from the classification-based DARTS: 1) inaccurate class-specific features; 2) partial target attention; and 3) blurred semantic regions. To tackle them in one shot, we propose Semantic-DARTS, combining the masked image modeling (MIM) paradigm with the classification task to incorporate local semantic information into the architecture search. Specifically, we design a lightweight reconstruction head that recovers the corrupted image based on the condensed latent feature, which learns both the local semantics and their relationship patch-wisely. Simultaneously, the concurrent classification head strengthens the connection between the global category of the target and the local semantics of their parts. As evidenced by our experiments, the proposed approach achieves state-of-the-art results on CIFAR-10, CIFAR-100, and ImageNet. Furthermore, the searched model is not only able to improve global class-specific features but also to capture fine-grained local representations, improving both the classification performance and the generalization ability.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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".