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Interpolated Adversarial Training: Achieving Robust Neural Networks Without Sacrificing Too Much Accuracy., , , и . AISec@CCS, стр. 95-103. ACM, (2019)Algorithms for Data-Efficient Training of Deep Neural Networks.. Aalto University, Espoo, Finland, (2020)base-search.net (ftaaltouniv:oai:aaltodoc.aalto.fi:123456789/59150).Towards Domain-Agnostic Contrastive Learning., , , , и . ICML, том 139 из Proceedings of Machine Learning Research, стр. 10530-10541. PMLR, (2021)PatchUp: A Feature-Space Block-Level Regularization Technique for Convolutional Neural Networks., , , , и . AAAI, стр. 589-597. AAAI Press, (2022)Adversarial Mixup Resynthesizers., , , , , , и . DGS@ICLR, OpenReview.net, (2019)SketchTransfer: A Challenging New Task for Exploring Detail-Invariance and the Abstractions Learned by Deep Networks., , , и . WACV, стр. 952-961. IEEE, (2020)Towards Domain-Agnostic Contrastive Learning., , , , и . CoRR, (2020)Interpolation-Based Semi-Supervised Learning for Object Detection., , , , и . CVPR, стр. 11602-11611. Computer Vision Foundation / IEEE, (2021)A Fair Basis For Charge Back., и . Int. CMG Conference, стр. 733-741. Computer Measurement Group, (1992)Analyzing and Classifying Malware Types on Windows Platform using an Ensemble Machine Learning Approach., , и . Int. J. Perform. Eng., 20 (5): 312 (2024)