AdaptIR: Parameter Efficient Multi-task Adaptation for Pre-trained Image
  Restoration Models
Hang Guo Tao Dai Yuanchao Bai Bin Chen Shu-Tao Xia Zexuan Zhu
Pre-training has shown promising results on various image restoration tasks,
which is usually followed by full fine-tuning for each specific downstream task
(e.g., image denoising). However, such full fine-tuning usually suffers from
the problems of heavy computational cost in practice, due to the massive
parameters of pre-trained restoration models, thus limiting its real-world
applications. Recently, Parameter Efficient Transfer Learning (PETL) offers an
efficient alternative solution to full fine-tuning, yet still faces great
challenges for pre-trained image restoration models, due to the diversity of
different degradations. To address these issues, we propose AdaptIR, a novel
parameter efficient transfer learning method for adapting pre-trained
restoration models. Specifically, the proposed method consists of a
multi-branch inception structure to orthogonally capture local spatial, global
spatial, and channel interactions. In this way, it allows powerful
representations under a very low parameter budget. Extensive experiments
demonstrate that the proposed method can achieve comparable or even better
performance than full fine-tuning, while only using 0.6% parameters. Code is
available at https://github.com/csguoh/AdaptIR.