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Intriguing Properties of Adversarial Training at Scale.

, и . ICLR, OpenReview.net, (2020)

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Mingliang Xie

Jian Xie

Yangpei Xie

Zhenwei Xie

Qian Xie

 

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Learning Transferable Adversarial Examples via Ghost Networks., , , , , и . AAAI, стр. 11458-11465. AAAI Press, (2020)Single-Shot Object Detection With Enriched Semantics., , , , , и . CVPR, стр. 5813-5821. Computer Vision Foundation / IEEE Computer Society, (2018)Improving Transferability of Adversarial Examples With Input Diversity., , , , , , и . CVPR, стр. 2730-2739. Computer Vision Foundation / IEEE, (2019)Consistency-Guided Meta-learning for Bootstrapping Semi-supervised Medical Image Segmentation., , , , , , и . MICCAI (4), том 14223 из Lecture Notes in Computer Science, стр. 183-193. Springer, (2023)Mamba-R: Vision Mamba ALSO Needs Registers., , , , , , , , и . CoRR, (2024)SwinMM: Masked Multi-view with Swin Transformers for 3D Medical Image Segmentation., , , , , , , , , и . MICCAI (3), том 14222 из Lecture Notes in Computer Science, стр. 486-496. Springer, (2023)Can CNNs Be More Robust Than Transformers?, , , и . ICLR, OpenReview.net, (2023)Scaling (Down) CLIP: A Comprehensive Analysis of Data, Architecture, and Training Strategies., , и . CoRR, (2024)Simulated Adversarial Testing of Face Recognition Models., , , , , , и . CoRR, (2021)Regional Homogeneity: Towards Learning Transferable Universal Adversarial Perturbations Against Defenses., , , , , и . CoRR, (2019)