Semantic-DARTS: Elevating Semantic Learning for Mobile Differentiable Architecture Search

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.