Towards Boosting Out-of-Distribution Detection from a Spatial Feature Importance Perspective

Yao Zhu, Xiu Yan, Chuanlong Xie

Abstract: In ensuring the reliable and secure operation of models, Out-of-Distribution (OOD) detection has gained widespread attention in recent years. Researchers have proposed various promising detection criteria to construct the rejection region of the model, treating samples falling into this region as out-of-distribution. However, these detection criteria are computed using all dense features of the model (before the pooling layer), overlooking the fact that different features may exhibit varying importance in the decision-making process. To this end, we first propose quantifying the contribution of different spatial positions in the dense features to the decision result with the Shapley Value, thereby obtaining the feature importance map. This spatial-oriented feature attribution method, compared to the classical channel-oriented feature attribution method that linearly combines weights with each activation map, achieves superior visual performance and fidelity in interpreting the decision-making process. Subsequently, we introduce a spatial feature purification method that removes spatial features with low importance in dense features and advocates using these purified features to compute detection criteria. Extensive experiments demonstrate the effectiveness of spatial feature purification in enhancing the performance of various existing detection methods. Notably, spatial feature purification boosts Energy Score and NNGuide on the ImageNet benchmark by 18.39\(\%\) and 26.45\(\%\) in average FPR95, respectively.

External IDs:dblp:journals/ijcv/ZhuYX25