8 citations · 23 across the 6 of their papers we have counts for
4 papers · 1 filter
Learning to Augment Distributions for Out-of-Distribution Detection
Qizhou Wang, Zhen Fang, Yonggang Zhang +3
Open-world classification systems should discern out-of-distribution (OOD) data whose labels deviate from those of in-distribution (ID) cases, motivating recent studies in OOD dete…
Out-of-distribution Detection Learning with Unreliable Out-of-distribution Sources
Haotian Zheng, Qizhou Wang, Zhen Fang +4
Out-of-distribution (OOD) detection discerns OOD data where the predictor cannot make valid predictions as in-distribution (ID) data, thereby increasing the reliability of open-wor…
SODA: Robust Training of Test-Time Data Adaptors
Zige Wang, Yonggang Zhang, Zhen Fang +3
Adapting models deployed to test distributions can mitigate the performance degradation caused by distribution shifts. However, privacy concerns may render model parameters inacces…
Invariant Learning via Probability of Sufficient and Necessary Causes
Mengyue Yang, Zhen Fang, Yonggang Zhang +5
Out-of-distribution (OOD) generalization is indispensable for learning models in the wild, where testing distribution typically unknown and different from the training. Recent meth…