87 citations · 90 across the 6 of their papers we have counts for
7 papers
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…
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…
Semantically Coherent Out-of-Distribution Detection
Jingkang Yang, Haoqi Wang, Litong Feng +4
Current out-of-distribution (OOD) detection benchmarks are commonly built by defining one dataset as in-distribution (ID) and all others as OOD. However, these benchmarks unfortuna…
Progressive Representative Labeling for Deep Semi-Supervised Learning
Xiaopeng Yan, Riquan Chen, Litong Feng +3
Deep semi-supervised learning (SSL) has experienced significant attention in recent years, to leverage a huge amount of unlabeled data to improve the performance of deep learning w…
Webly Supervised Image Classification with Metadata: Automatic Noisy Label Correction via Visual-Semantic Graph
Jingkang Yang, Weirong Chen, Litong Feng +3
Webly supervised learning becomes attractive recently for its efficiency in data expansion without expensive human labeling. However, adopting search queries or hashtags as web lab…
Webly Supervised Image Classification with Self-Contained Confidence
Jingkang Yang, Litong Feng, Weirong Chen +4
This paper focuses on webly supervised learning (WSL), where datasets are built by crawling samples from the Internet and directly using search queries as web labels. Although WSL…