1 code implementation • 4 Apr 2024 • Tianqi Li, Guansong Pang, Xiao Bai, Wenjun Miao, Jin Zheng
Existing prompt learning methods have shown certain capabilities in Out-of-Distribution (OOD) detection, but the lack of OOD images in the target dataset in their training can lead to mismatches between OOD images and In-Distribution (ID) categories, resulting in a high false positive rate.
Out-of-Distribution Detection Out of Distribution (OOD) Detection
1 code implementation • 17 Dec 2023 • Wenjun Miao, Guansong Pang, Tianqi Li, Xiao Bai, Jin Zheng
To this end, we introduce a novel calibrated outlier class learning (COCL) approach, in which 1) a debiased large margin learning method is introduced in the outlier class learning to distinguish OOD samples from both head and tail classes in the representation space and 2) an outlier-class-aware logit calibration method is defined to enhance the long-tailed classification confidence.