@inproceedings{
wang2024sfp,
title={{SFP}: Spurious Feature-Targeted Pruning for Out-of-Distribution Generalization},
author={Yingchun Wang and Jingcai Guo and Song Guo and LIU Yi and Jie ZHANG and Weizhan Zhang},
booktitle={ACM Multimedia 2024},
year={2024},
url={https://openreview.net/forum?id=DjcQVopjzX}
}
Recent studies reveal that even highly biased dense networks can contain an invariant substructure with superior out-of-distribution (OOD) generalization. While existing works commonly seek these substructures using global sparsity constraints, the uniform imposition of sparse penalties across samples with diverse levels of spurious contents renders such methods suboptimal. The precise adaptation of model sparsity, specifically tailored for spurious features, remains a significant challenge. Motivated by the insight that in-distribution (ID) data containing spurious features may exhibit lower experiential risk, we propose a novel **S**purious **F**eature-targeted **P**runing framework, dubbed **SFP**, to induce the authentic invariant substructures without referring to the above concerns. Specifically, SFP distinguishes spurious features within ID instances during training by a theoretically validated threshold. It then penalizes the corresponding feature projections onto the model space, steering the optimization towards subspaces spanned by those invariant factors. Moreover, we also conduct detailed theoretical analysis to provide a rationality guarantee and a proof framework for OOD structures based on model sparsity. Experiments on various OOD datasets show that SFP can significantly outperform both structure-based and non-structure-based OOD generalization SOTAs by large margins.
SFP: Spurious Feature-Targeted Pruning for Out-of-Distribution Generalization
Yingchun Wang Jingcai Guo Song Guo LIU Yi Jie ZHANG Weizhan Zhang