3 citations · 4 across the 3 of their papers we have counts for
3 papers
cs.LG2023★ 1 cited
Hard Adversarial Example Mining for Improving Robust Fairness
Chenhao Lin, Xiang Ji, Yulong Yang +4
Adversarial training (AT) is widely considered the state-of-the-art technique for improving the robustness of deep neural networks (DNNs) against adversarial examples (AE). Neverth…
cs.LG2023
CILIATE: Towards Fairer Class-based Incremental Learning by Dataset and Training Refinement
Xuanqi Gao, Juan Zhai, Shiqing Ma +3
Due to the model aging problem, Deep Neural Networks (DNNs) need updates to adjust them to new data distributions. The common practice leverages incremental learning (IL), e.g., Cl…
cs.CV2023★ 3 cited
End-to-end Face-swapping via Adaptive Latent Representation Learning
Chenhao Lin, Pengbin Hu, Chao Shen +1
Taking full advantage of the excellent performance of StyleGAN, style transfer-based face swapping methods have been extensively investigated recently. However, these studies requi…