Dyn-Adapter: Towards Disentangled Representation for Efficient Visual Recognition

Yurong Zhang, Honghao Chen, Xinyu Zhang, Xiangxiang Chu, Kaiqi Huang, Li Song

Keywords: parameter-efficient transfer learning, dynamic adapter, inference efficiency

Abstract: Parameter-efficient transfer learning (PETL) is a promising task, aiming to adapt the large-scale pretrained model to downstream tasks with a relatively modest cost. However, current PETL methods struggle in compressing computational complexity and bear 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 generalized 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. We will make the code publicly available.

Primary Area: transfer learning, meta learning, and lifelong learning

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Submission Number: 3382