176 citations · 998 across the 76 of their papers we have counts for
4 papers · 2 filters
Finetuning Text-to-Image Diffusion Models for Fairness
Xudong Shen, Chao Du, Tianyu Pang +3
The rapid adoption of text-to-image diffusion models in society underscores an urgent need to address their biases. Without interventions, these biases could propagate a skewed wor…
Distill to Delete: Unlearning in Graph Networks with Knowledge Distillation
Yash Sinha, Murari Mandal, Mohan Kankanhalli
Graph unlearning has emerged as a pivotal method to delete information from a pre-trained graph neural network (GNN). One may delete nodes, a class of nodes, edges, or a class of e…
Enhancing Adversarial Contrastive Learning via Adversarial Invariant Regularization
Xilie Xu, Jingfeng Zhang, Feng Liu +2
Adversarial contrastive learning (ACL) is a technique that enhances standard contrastive learning (SCL) by incorporating adversarial data to learn a robust representation that can…
Efficient Adversarial Contrastive Learning via Robustness-Aware Coreset Selection
Xilie Xu, Jingfeng Zhang, Feng Liu +2
Adversarial contrastive learning (ACL) does not require expensive data annotations but outputs a robust representation that withstands adversarial attacks and also generalizes to a…