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20162023
most citedFree Lunch for Domain Adversarial Training: Environment Label Smoothing

24 citations · 70 across the 8 of their papers we have counts for

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Showing 2023Show all

5 papers · 1 filter

cs.LG2023

Model-free Test Time Adaptation for Out-Of-Distribution Detection

YiFan Zhang, Xue Wang, Tian Zhou +5

Out-of-distribution (OOD) detection is essential for the reliability of ML models. Most existing methods for OOD detection learn a fixed decision criterion from a given in-distribu…

cs.CV2023

Illumination Distillation Framework for Nighttime Person Re-Identification and A New Benchmark

Andong Lu, Zhang Zhang, Yan Huang +4

Nighttime person Re-ID (person re-identification in the nighttime) is a very important and challenging task for visual surveillance but it has not been thoroughly investigated. Und…

cs.LG2023★ 14 cited

AdaNPC: Exploring Non-Parametric Classifier for Test-Time Adaptation

Yi-Fan Zhang, Xue Wang, Kexin Jin +5

Many recent machine learning tasks focus to develop models that can generalize to unseen distributions. Domain generalization (DG) has become one of the key topics in various field…

cs.CV2023★ 9 cited

Semantic Prompt for Few-Shot Image Recognition

Wentao Chen, Chenyang Si, Zhang Zhang +3

Few-shot learning is a challenging problem since only a few examples are provided to recognize a new class. Several recent studies exploit additional semantic information, e.g. tex…

cs.LG2023★ 24 cited

Free Lunch for Domain Adversarial Training: Environment Label Smoothing

YiFan Zhang, Xue Wang, Jian Liang +4

A fundamental challenge for machine learning models is how to generalize learned models for out-of-distribution (OOD) data. Among various approaches, exploiting invariant features…