2 citations · 3 across the 7 of their papers we have counts for
8 papers
Zero-Shot Machine Unlearning with Proxy Adversarial Data Generation
Huiqiang Chen, Tianqing Zhu, Xin Yu +1
Machine unlearning aims to remove the influence of specific samples from a trained model. A key challenge in this process is over-unlearning, where the model's performance on the r…
Safe and Reliable Diffusion Models via Subspace Projection
Huiqiang Chen, Tianqing Zhu, Linlin Wang +3
Large-scale text-to-image (T2I) diffusion models have revolutionized image generation, enabling the synthesis of highly detailed visuals from textual descriptions. However, these m…
AFed: Algorithmic Fair Federated Learning
Huiqiang Chen, Tianqing Zhu, Wanlei Zhou +1
Federated Learning (FL) has gained significant attention as it facilitates collaborative machine learning among multiple clients without centralizing their data on a server. FL ens…
MM-WLAuslan: Multi-View Multi-Modal Word-Level Australian Sign Language Recognition Dataset
Xin Shen, Heming Du, Hongwei Sheng +9
Isolated Sign Language Recognition (ISLR) focuses on identifying individual sign language glosses. Considering the diversity of sign languages across geographical regions, developi…
Machine Unlearning via Null Space Calibration
Huiqiang Chen, Tianqing Zhu, Xin Yu +1
Machine unlearning aims to enable models to forget specific data instances when receiving deletion requests. Current research centres on efficient unlearning to erase the influence…
Divide and Ensemble: Progressively Learning for the Unknown
Hu Zhang, Xin Shen, Heming Du +10
In the wheat nutrient deficiencies classification challenge, we present the DividE and EnseMble (DEEM) method for progressive test data predictions. We find that (1) test images ar…
Privacy and Fairness in Federated Learning: on the Perspective of Trade-off
Huiqiang Chen, Tianqing Zhu, Tao Zhang +2
Federated learning (FL) has been a hot topic in recent years. Ever since it was introduced, researchers have endeavored to devise FL systems that protect privacy or ensure fair res…
Model Inversion Attack against Transfer Learning: Inverting a Model without Accessing It
Dayong Ye, Huiqiang Chen, Shuai Zhou +3
Transfer learning is an important approach that produces pre-trained teacher models which can be used to quickly build specialized student models. However, recent research on trans…