99 citations · 291 across the 9 of their papers we have counts for
13 papers
Learning to Augment via Implicit Differentiation for Domain Generalization
Tingwei Wang, Da Li, Kaiyang Zhou +2
Machine learning models are intrinsically vulnerable to domain shift between training and testing data, resulting in poor performance in novel domains. Domain generalization (DG) a…
OpenOOD: Benchmarking Generalized Out-of-Distribution Detection
Jingkang Yang, Pengyun Wang, Dejian Zou +13
Out-of-distribution (OOD) detection is vital to safety-critical machine learning applications and has thus been extensively studied, with a plethora of methods developed in the lit…
Unified Vision and Language Prompt Learning
Yuhang Zang, Wei Li, Kaiyang Zhou +2
Prompt tuning, a parameter- and data-efficient transfer learning paradigm that tunes only a small number of parameters in a model's input space, has become a trend in the vision co…
Full-Spectrum Out-of-Distribution Detection
Jingkang Yang, Kaiyang Zhou, Ziwei Liu
Existing out-of-distribution (OOD) detection literature clearly defines semantic shift as a sign of OOD but does not have a consensus over covariate shift. Samples experiencing cov…
Domain Attention Consistency for Multi-Source Domain Adaptation
Zhongying Deng, Kaiyang Zhou, Yongxin Yang +1
Most existing multi-source domain adaptation (MSDA) methods minimize the distance between multiple source-target domain pairs via feature distribution alignment, an approach borrow…
Energy-Based Open-World Uncertainty Modeling for Confidence Calibration
Yezhen Wang, Bo Li, Tong Che +3
Confidence calibration is of great importance to the reliability of decisions made by machine learning systems. However, discriminative classifiers based on deep neural networks ar…