UniPT: Universal Parallel Tuning for Transfer Learning with Efficient Parameter and Memory
Authors: Haiwen Diao, Bo Wan, Ying Zhang, Xu Jia, Huchuan Lu, Long Chen

Parameter-efficient transfer learning (PETL) i.e. fine-tuning a small portion of parameters is an effective strategy for adapting pre-trained models to downstream domains. To further reduce the memory demand recent PETL works focus on the more valuable memory-efficient characteristic. In this paper we argue that the scalability adaptability and generalizability of state-of-the-art methods are hindered by structural dependency and pertinency on specific pre-trained backbones. To this end we propose a new memory-efficient PETL strategy Universal Parallel Tuning (UniPT) to mitigate these weaknesses. Specifically we facilitate the transfer process via a lightweight and learnable parallel network which consists of: 1) A parallel interaction module that decouples the sequential connections and processes the intermediate activations detachedly from the pre-trained network. 2) A confidence aggregation module that learns optimal strategies adaptively for integrating cross-layer features. We evaluate UniPT with different backbones (e.g. T5 VSEinfinity CLIP4Clip Clip-ViL and MDETR) on various vision-and-language and pure NLP tasks. Extensive ablations on 18 datasets have validated that UniPT can not only dramatically reduce memory consumption and outperform the best competitor but also achieve competitive performance over other plain PETL methods with lower training memory overhead. Our code is publicly available at: https://github.com/Paranioar/UniPT.