118 citations · 649 across the 48 of their papers we have counts for
8 papers · 1 filter
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…
Fed-CBS: A Heterogeneity-Aware Client Sampling Mechanism for Federated Learning via Class-Imbalance Reduction
Jianyi Zhang, Ang Li, Minxue Tang +6
Due to limited communication capacities of edge devices, most existing federated learning (FL) methods randomly select only a subset of devices to participate in training for each…
FADE: Enabling Federated Adversarial Training on Heterogeneous Resource-Constrained Edge Devices
Minxue Tang, Jianyi Zhang, Mingyuan Ma +5
Federated adversarial training can effectively complement adversarial robustness into the privacy-preserving federated learning systems. However, the high demand for memory capacit…