Exploring Representation Invariance in Finetuning

Wenqiang Zu; Shenghao Xie; Hao Chen; Zhiqiang Chen; Liwen Hu; Yuanhao Xi; Yiming Liang; Junliang Ye; Bo Lei; Tiejun Huang; Guoqi Li; Lei Ma

arXiv · 2503.07399

Exploring Representation Invariance in Finetuning

Wenqiang Zu·Shenghao Xie·Hao Chen·Zhiqiang Chen·Liwen Hu·Yuanhao Xi·Yiming Liang·Junliang Ye·Bo Lei·Tiejun Huang·Guoqi Li·Lei Ma

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Abstract

Foundation models pretrained on large-scale natural images are widely adapted to various cross-domain low-resource downstream tasks, benefiting from generalizable and transferable patterns captured by their representations. However, these representations are later found to gradually vanish during finetuning, accompanied by a degradation of model's original generalizability. In this paper, we argue that such tasks can be effectively adapted without sacrificing the benefits of pretrained representations. We approach this by introducing \textit{Representation Invariance FineTuning (RIFT)}, a regularization that maximizes the representation similarity between pretrained and finetuned models by leveraging orthogonal invariance of manifolds in a computationally efficient way. Experiments demonstrate that our method is compatible with mainstream finetuning methods, offering competitive or even enhanced performance and better preservation of the generalizability.

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BibTeX RIS

Wenqiang Zu, Shenghao Xie, Hao Chen, Zhiqiang Chen, Liwen Hu, Yuanhao Xi, Yiming Liang, Junliang Ye, Bo Lei, Tiejun Huang, Guoqi Li, Lei Ma. 2025-03-10. Exploring Representation Invariance in Finetuning. https://arxiv.org/abs/2503.07399

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