no code implementations • 2 May 2024 • Chris Xing Tian, Yibing Liu, Haoliang Li, Ray C. C. Cheung, Shiqi Wang
However, FL also faces challenges such as high computational and communication costs regarding resource-constrained devices, and poor generalization performance due to the heterogeneity of data across edge clients and the presence of out-of-distribution data.
1 code implementation • 5 Jun 2023 • Yibing Liu, Chris Xing Tian, Haoliang Li, Lei Ma, Shiqi Wang
The out-of-distribution (OOD) problem generally arises when neural networks encounter data that significantly deviates from the training data distribution, i. e., in-distribution (InD).
Ranked #2 on Out-of-Distribution Detection on ImageNet-1k vs Textures (AUROC metric)
no code implementations • 13 Nov 2022 • Yibing Liu, Chris Xing Tian, Haoliang Li, Shiqi Wang
Specifically, by treating feature elements as neuron activation states, we show that conventional alignment methods tend to deteriorate the diversity of learned invariant features, as they indiscriminately minimize all neuron activation differences.
no code implementations • 14 May 2021 • Chris Xing Tian, Haoliang Li, YuFei Wang, Shiqi Wang
However, due to the issue of limited dataset availability and the strict legal and ethical requirements for patient privacy protection, the broad applications of medical imaging classification driven by DNN with large-scale training data have been largely hindered.
no code implementations • 27 Feb 2021 • Chris Xing Tian, Haoliang Li, Xiaofei Xie, Yang Liu, Shiqi Wang
More specifically, by treating the DNN as a program and each neuron as a functional point of the code, during the network training we aim to improve the generalization capability by maximizing the neuron coverage of DNN with the gradient similarity regularization between the original and augmented samples.