3 citations · 6 across the 7 of their papers we have counts for
7 papers
FedCompetitors: Harmonious Collaboration in Federated Learning with Competing Participants
Shanli Tan, Hao Cheng, Xiaohu Wu +5
Federated learning (FL) provides a privacy-preserving approach for collaborative training of machine learning models. Given the potential data heterogeneity, it is crucial to selec…
DOS: Diverse Outlier Sampling for Out-of-Distribution Detection
Wenyu Jiang, Hao Cheng, Mingcai Chen +2
Modern neural networks are known to give overconfident prediction for out-of-distribution inputs when deployed in the open world. It is common practice to leverage a surrogate outl…
MixBoost: Improving the Robustness of Deep Neural Networks by Boosting Data Augmentation
Zhendong Liu, Wenyu Jiang, Min guo +1
As more and more artificial intelligence (AI) technologies move from the laboratory to real-world applications, the open-set and robustness challenges brought by data from the real…
Explanation-based Counterfactual Retraining(XCR): A Calibration Method for Black-box Models
Liu Zhendong, Wenyu Jiang, Yi Zhang +1
With the rapid development of eXplainable Artificial Intelligence (XAI), a long line of past work has shown concerns about the Out-of-Distribution (OOD) problem in perturbation-bas…
READ: Aggregating Reconstruction Error into Out-of-distribution Detection
Wenyu Jiang, Yuxin Ge, Hao Cheng +3
Detecting out-of-distribution (OOD) samples is crucial to the safe deployment of a classifier in the real world. However, deep neural networks are known to be overconfident for abn…
Completing Partial Point Clouds with Outliers by Collaborative Completion and Segmentation
Changfeng Ma, Yang Yang, Jie Guo +2
Most existing point cloud completion methods are only applicable to partial point clouds without any noises and outliers, which does not always hold in practice. We propose in this…
Tailor Versatile Multi-modal Learning for Multi-label Emotion Recognition
Yi Zhang, Mingyuan Chen, Jundong Shen +1
Multi-modal Multi-label Emotion Recognition (MMER) aims to identify various human emotions from heterogeneous visual, audio and text modalities. Previous methods mainly focus on pr…