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Computer Science > Computer Vision and Pattern Recognition

arXiv:2203.16329 (cs)
[Submitted on 29 Mar 2022 (v1), last revised 13 Jul 2023 (this version, v3)]

Title:Parameter-efficient Model Adaptation for Vision Transformers

Authors:Xuehai He, Chunyuan Li, Pengchuan Zhang, Jianwei Yang, Xin Eric Wang
View a PDF of the paper titled Parameter-efficient Model Adaptation for Vision Transformers, by Xuehai He and 4 other authors
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Abstract:In computer vision, it has achieved great transfer learning performance via adapting large-scale pretrained vision models (e.g., vision transformers) to downstream tasks. Common approaches for model adaptation either update all model parameters or leverage linear probes. In this paper, we aim to study parameter-efficient model adaptation strategies for vision transformers on the image classification task. We formulate efficient model adaptation as a subspace training problem and perform a comprehensive benchmarking over different efficient adaptation methods. We conduct an empirical study on each efficient model adaptation method focusing on its performance alongside parameter cost. Furthermore, we propose a parameter-efficient model adaptation framework, which first selects submodules by measuring local intrinsic dimensions and then projects them into subspace for further decomposition via a novel Kronecker Adaptation (KAdaptation) method. We analyze and compare our method with a diverse set of baseline model adaptation methods (including state-of-the-art methods for pretrained language models). Our method performs the best in terms of the tradeoff between accuracy and parameter efficiency across 20 image classification datasets under the few-shot setting and 7 image classification datasets under the full-shot setting.
Subjects: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
Cite as: arXiv:2203.16329 [cs.CV]
  (or arXiv:2203.16329v3 [cs.CV] for this version)
  https://doi.org/10.48550/arXiv.2203.16329
arXiv-issued DOI via DataCite

Submission history

From: Xuehai He [view email]
[v1] Tue, 29 Mar 2022 05:30:09 UTC (433 KB)
[v2] Sun, 4 Dec 2022 08:35:47 UTC (544 KB)
[v3] Thu, 13 Jul 2023 22:12:10 UTC (544 KB)
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