Rethinking Efficient Tuning Methods from a Unified Perspective

• URL: http://arxiv.org/abs/2303.00690v1
• Date: Wed, 1 Mar 2023 17:38:03 GMT
• Title: Rethinking Efficient Tuning Methods from a Unified Perspective
• Authors: Zeyinzi Jiang, Chaojie Mao, Ziyuan Huang, Yiliang Lv, Deli Zhao, Jingren Zhou
• Abstract summary: We revisit the design paradigm of PETL and derive a unified framework U-Tuning for parameter-efficient transfer learning. The U-Tuning framework can simultaneously encompass existing methods and derive new approaches for parameter-efficient transfer learning.
• Score: 34.67645496324432
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Parameter-efficient transfer learning (PETL) based on large-scale pre-trained foundation models has achieved great success in various downstream applications. Existing tuning methods, such as prompt, prefix, and adapter, perform task-specific lightweight adjustments to different parts of the original architecture. However, they take effect on only some parts of the pre-trained models, i.e., only the feed-forward layers or the self-attention layers, which leaves the remaining frozen structures unable to adapt to the data distributions of downstream tasks. Further, the existing structures are strongly coupled with the Transformers, hindering parameter-efficient deployment as well as the design flexibility for new approaches. In this paper, we revisit the design paradigm of PETL and derive a unified framework U-Tuning for parameter-efficient transfer learning, which is composed of an operation with frozen parameters and a unified tuner that adapts the operation for downstream applications. The U-Tuning framework can simultaneously encompass existing methods and derive new approaches for parameter-efficient transfer learning, which prove to achieve on-par or better performances on CIFAR-100 and FGVC datasets when compared with existing PETL methods.

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