Three Factors to Improve Out-of-Distribution Detection
Hyunjun Choi JaeHo Chung Hawook Jeong Jin Young Choi
In the problem of out-of-distribution (OOD) detection, the usage of auxiliary
data as outlier data for fine-tuning has demonstrated encouraging performance.
However, previous methods have suffered from a trade-off between classification
accuracy (ACC) and OOD detection performance (AUROC, FPR, AUPR). To improve
this trade-off, we make three contributions: (i) Incorporating a self-knowledge
distillation loss can enhance the accuracy of the network; (ii) Sampling
semi-hard outlier data for training can improve OOD detection performance with
minimal impact on accuracy; (iii) The introduction of our novel supervised
contrastive learning can simultaneously improve OOD detection performance and
the accuracy of the network. By incorporating all three factors, our approach
enhances both accuracy and OOD detection performance by addressing the
trade-off between classification and OOD detection. Our method achieves
improvements over previous approaches in both performance metrics.