Web: http://arxiv.org/abs/2209.06971

Sept. 16, 2022, 1:15 a.m. | Junxuan Huang, Yatong An, Lu cheng, Bai Chen, Junsong Yuan, Chunming Qiao

cs.CV updates on arXiv.org arxiv.org

Despite recent success of self-supervised based contrastive learning model
for 3D point clouds representation, the adversarial robustness of such
pre-trained models raised concerns. Adversarial contrastive learning (ACL) is
considered an effective way to improve the robustness of pre-trained models. In
contrastive learning, the projector is considered an effective component for
removing unnecessary feature information during contrastive pretraining and
most ACL works also use contrastive loss with projected feature representations
to generate adversarial examples in pretraining, while "unprojected " feature
representations …

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