87 citations · 253 across the 14 of their papers we have counts for
14 papers
OpenOOD v1.5: Enhanced Benchmark for Out-of-Distribution Detection
Jingyang Zhang, Jingkang Yang, Pengyun Wang +9
Out-of-Distribution (OOD) detection is critical for the reliable operation of open-world intelligent systems. Despite the emergence of an increasing number of OOD detection methods…
Feed Two Birds with One Scone: Exploiting Wild Data for Both Out-of-Distribution Generalization and Detection
Haoyue Bai, Gregory Canal, Xuefeng Du +3
Modern machine learning models deployed in the wild can encounter both covariate and semantic shifts, giving rise to the problems of out-of-distribution (OOD) generalization and OO…
Non-Parametric Outlier Synthesis
Leitian Tao, Xuefeng Du, Xiaojin Zhu +1
Out-of-distribution (OOD) detection is indispensable for safely deploying machine learning models in the wild. One of the key challenges is that models lack supervision signals fro…
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…
Unknown-Aware Object Detection: Learning What You Don't Know from Videos in the Wild
Xuefeng Du, Xin Wang, Gabriel Gozum +1
Building reliable object detectors that can detect out-of-distribution (OOD) objects is critical yet underexplored. One of the key challenges is that models lack supervision signal…
VOS: Learning What You Don't Know by Virtual Outlier Synthesis
Xuefeng Du, Zhaoning Wang, Mu Cai +1
Out-of-distribution (OOD) detection has received much attention lately due to its importance in the safe deployment of neural networks. One of the key challenges is that models lac…
Noise-robust Graph Learning by Estimating and Leveraging Pairwise Interactions
Xuefeng Du, Tian Bian, Yu Rong +6
Teaching Graph Neural Networks (GNNs) to accurately classify nodes under severely noisy labels is an important problem in real-world graph learning applications, but is currently u…
Active Learning to Classify Macromolecular Structures in situ for Less Supervision in Cryo-Electron Tomography
Xuefeng Du, Haohan Wang, Zhenxi Zhu +4
Motivation: Cryo-Electron Tomography (cryo-ET) is a 3D bioimaging tool that visualizes the structural and spatial organization of macromolecules at a near-native state in single ce…
Learning Diverse-Structured Networks for Adversarial Robustness
Xuefeng Du, Jingfeng Zhang, Bo Han +5
In adversarial training (AT), the main focus has been the objective and optimizer while the model has been less studied, so that the models being used are still those classic ones…
Small-Group Learning, with Application to Neural Architecture Search
Xuefeng Du, Pengtao Xie
In human learning, an effective learning methodology is small-group learning: a small group of students work together towards the same learning objective, where they express their…
Skillearn: Machine Learning Inspired by Humans' Learning Skills
Pengtao Xie, Xuefeng Du, Hao Ban
Humans, as the most powerful learners on the planet, have accumulated a lot of learning skills, such as learning through tests, interleaving learning, self-explanation, active reca…
Learning by Passing Tests, with Application to Neural Architecture Search
Xuefeng Du, Haochen Zhang, Pengtao Xie
Learning through tests is a broadly used methodology in human learning and shows great effectiveness in improving learning outcome: a sequence of tests are made with increasing lev…
Node Classification on Graphs with Few-Shot Novel Labels via Meta Transformed Network Embedding
Lin Lan, Pinghui Wang, Xuefeng Du +3
We study the problem of node classification on graphs with few-shot novel labels, which has two distinctive properties: (1) There are novel labels to emerge in the graph; (2) The n…
Towards Efficient Unconstrained Palmprint Recognition via Deep Distillation Hashing
Huikai Shao, Dexing Zhong, Xuefeng Du
Deep palmprint recognition has become an emerging issue with great potential for personal authentication on handheld and wearable consumer devices. Previous studies of palmprint re…