16 citations · 20 across the 4 of their papers we have counts for
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cs.CV2023★ 2 cited
Latent Feature Relation Consistency for Adversarial Robustness
Xingbin Liu, Huafeng Kuang, Hong Liu +3
Deep neural networks have been applied in many computer vision tasks and achieved state-of-the-art performance. However, misclassification will occur when DNN predicts adversarial…
cs.CV2023★ 1 cited
CAT:Collaborative Adversarial Training
Xingbin Liu, Huafeng Kuang, Xianming Lin +2
Adversarial training can improve the robustness of neural networks. Previous methods focus on a single adversarial training strategy and do not consider the model property trained…
cs.CV2022★ 16 cited
Exploring Target Representations for Masked Autoencoders
Xingbin Liu, Jinghao Zhou, Tao Kong +2
Masked autoencoders have become popular training paradigms for self-supervised visual representation learning. These models randomly mask a portion of the input and reconstruct the…