118 citations · 649 across the 48 of their papers we have counts for
48 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…
SIO: Synthetic In-Distribution Data Benefits Out-of-Distribution Detection
Jingyang Zhang, Nathan Inkawhich, Randolph Linderman +3
Building up reliable Out-of-Distribution (OOD) detectors is challenging, often requiring the use of OOD data during training. In this work, we develop a data-driven approach which…
PIDS: Joint Point Interaction-Dimension Search for 3D Point Cloud
Tunhou Zhang, Mingyuan Ma, Feng Yan +2
The interaction and dimension of points are two important axes in designing point operators to serve hierarchical 3D models. Yet, these two axes are heterogeneous and challenging t…
Federated Unsupervised Visual Representation Learning via Exploiting General Content and Personal Style
Yuewei Yang, Jingwei Sun, Ang Li +2
Discriminative unsupervised learning methods such as contrastive learning have demonstrated the ability to learn generalized visual representations on centralized data. It is nonet…
Rethinking Normalization Methods in Federated Learning
Zhixu Du, Jingwei Sun, Ang Li +4
Federated learning (FL) is a popular distributed learning framework that can reduce privacy risks by not explicitly sharing private data. In this work, we explicitly uncover extern…
Fine-grain Inference on Out-of-Distribution Data with Hierarchical Classification
Randolph Linderman, Jingyang Zhang, Nathan Inkawhich +2
Machine learning methods must be trusted to make appropriate decisions in real-world environments, even when faced with out-of-distribution (OOD) samples. Many current approaches s…