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Rethinking softmax cross-entropy loss for adversarial robustness

T. "Pang, K. "Xu, Y. "Dong, C. "Du, N. "Chen, and J. "Zhu. (2019)

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Better Diffusion Models Further Improve Adversarial Training.Z. Wang, T. Pang, C. Du, M. Lin, W. Liu, and S. Yan. ICML, volume 202 of Proceedings of Machine Learning Research, page 36246-36263. PMLR, (2023)Edge Snapping-Based Depth Enhancement for Dynamic Occlusion Handling in Augmented Reality.C. Du, Y. Chen, M. Ye, and L. Ren. ISMAR, page 54-62. IEEE Computer Society, (2016)Mask-off: Synthesizing Face Images in the Presence of Head-mounted Displays.Y. Zhao, Q. Xu, W. Chen, C. Du, J. Xing, X. Huang, and R. Yang. VR, page 267-276. IEEE, (2019)Interactive Visual Hull Refinement for Specular and Transparent Object Surface Reconstruction.X. Zuo, C. Du, S. Wang, J. Zheng, and R. Yang. ICCV, page 2237-2245. IEEE Computer Society, (2015)An adaptive Viterbi decoder based on FPGA dynamic reconfiguration technology.X. Qin, M. Zhu, Z. Wei, and D. Chao. FPT, page 315-318. IEEE, (2004)To Relieve Your Headache of Training an MRF, Take AdVIL.C. Li, C. Du, K. Xu, M. Welling, J. Zhu, and B. Zhang. ICLR, OpenReview.net, (2020)Real-Time Simultaneous Pose and Shape Estimation for Articulated Objects Using a Single Depth Camera.M. Ye, Y. Shen, C. Du, Z. Pan, and R. Yang. IEEE Trans. Pattern Anal. Mach. Intell., 38 (8): 1517-1532 (2016)Exploration in Online Advertising Systems with Deep Uncertainty-Aware Learning.C. Du, Z. Gao, S. Yuan, L. Gao, Z. Li, Y. Zeng, X. Zhu, J. Xu, K. Gai, and K. Lee. KDD, page 2792-2801. ACM, (2021)A Background-based Data Enhancement Method for Lymphoma Segmentation in 3D PET Images.H. Hu, C. Du, Q. Guan, Q. Zhou, P. Vera, and S. Ruan. BIBM, page 1194-1196. IEEE, (2019)Collaborative Filtering Recommender Algorithm Based on Comments and Score.Y. Zhu, W. Song, L. Liu, X. Zhao, and C. Du. ISCID (1), page 304-307. IEEE, (2017)

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Rethinking softmax cross-entropy loss for adversarial robustness

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copydeleteadd this publication to your clipboardcommunity posthistory of this postURLDOIBibTeXEndNoteAPAChicagoDIN 1505HarvardMSOffice XML Rethinking softmax cross-entropy loss for adversarial robustness

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Rethinking softmax cross-entropy loss for adversarial robustness