Don't Forget What I did?: Assessing Client Contributions in Federated Learning

no code implementations • 11 Mar 2024 • Bishwamittra Ghosh, Debabrota Basu, Fu Huazhu, Wang Yuan, Renuga Kanagavelu, Jiang Jin Peng, Liu Yong, Goh Siow Mong Rick, Wei Qingsong

Additionally, to assess client contribution under limited computational budget, we propose a scheduling procedure that considers a two-sided fairness criteria to perform expensive Shapley value computation only in a subset of training epochs.

Data Poisoning Fairness +2

Learn from Concepts: Towards the Purified Memory for Few-shot Learning

no code implementations • Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence 2021 • Xuncheng Liu, Xudong Tian, Shaohui Lin, Yanyun Qu, Lizhuang Ma, Wang Yuan, Zhizhong Zhang, Yuan Xie

In this paper, we present a novel purified memory mechanism that simulates the recognition process of human beings.

Few-Shot Learning

Spoof Face Detection Via Semi-Supervised Adversarial Training

no code implementations • 22 May 2020 • Chengwei Chen, Wang Yuan, Xuequan Lu, Lizhuang Ma

To capture the underlying structure of live faces data in latent representation space, we propose to train the live face data only, with a convolutional Encoder-Decoder network acting as a Generator.

Face Detection Face Presentation Attack Detection +4

Novelty Detection via Non-Adversarial Generative Network

no code implementations • 3 Feb 2020 • Chengwei Chen, Wang Yuan, Yuan Xie, Yanyun Qu, Yiqing Tao, Haichuan Song, Lizhuang Ma

One-class novelty detection is the process of determining if a query example differs from the training examples (the target class).

Image Reconstruction Novelty Detection