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stat.ML2024
DSDE: Using Proportion Estimation to Improve Model Selection for Out-of-Distribution Detection
Jingyao Geng, Yuan Zhang, Jiaqi Huang +4
Model library is an effective tool for improving the performance of single-model Out-of-Distribution (OoD) detector, mainly through model selection and detector fusion. However, ex…
cs.LG2024
Provable Contrastive Continual Learning
Yichen Wen, Zhiquan Tan, Kaipeng Zheng +2
Continual learning requires learning incremental tasks with dynamic data distributions. So far, it has been observed that employing a combination of contrastive loss and distillati…
cs.LG2024
Enhancing Out-of-Distribution Detection with Multitesting-based Layer-wise Feature Fusion
Jiawei Li, Sitong Li, Shanshan Wang +3
Deploying machine learning in open environments presents the challenge of encountering diverse test inputs that differ significantly from the training data. These out-of-distributi…