Dyn-Adapter: Towards Disentangled Representation for Efficient Visual Recognition

• URL: http://arxiv.org/abs/2407.14302v2
• Date: Tue, 23 Jul 2024 07:57:17 GMT
• Title: Dyn-Adapter: Towards Disentangled Representation for Efficient Visual Recognition
• Authors: Yurong Zhang, Honghao Chen, Xinyu Zhang, Xiangxiang Chu, Li Song,
• Abstract summary: This paper presents an efficient visual recognition paradigm, called Dynamic Adapter (Dyn-Adapter) We devise a dynamic architecture with balanced early heads for multi-level feature extraction, along with adaptive training strategy. We reduce FLOPs during inference by 50%, while maintaining or even yielding higher recognition accuracy.
• Score: 22.615830919860777
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Parameter-efficient transfer learning (PETL) is a promising task, aiming to adapt the large-scale pre-trained model to downstream tasks with a relatively modest cost. However, current PETL methods struggle in compressing computational complexity and bear a heavy inference burden due to the complete forward process. This paper presents an efficient visual recognition paradigm, called Dynamic Adapter (Dyn-Adapter), that boosts PETL efficiency by subtly disentangling features in multiple levels. Our approach is simple: first, we devise a dynamic architecture with balanced early heads for multi-level feature extraction, along with adaptive training strategy. Second, we introduce a bidirectional sparsity strategy driven by the pursuit of powerful generalization ability. These qualities enable us to fine-tune efficiently and effectively: we reduce FLOPs during inference by 50%, while maintaining or even yielding higher recognition accuracy. Extensive experiments on diverse datasets and pretrained backbones demonstrate the potential of Dyn-Adapter serving as a general efficiency booster for PETL in vision recognition tasks.

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