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Parseval Networks: Improving Robustness to Adversarial Examples.

, , , , and . ICML, volume 70 of Proceedings of Machine Learning Research, page 854-863. PMLR, (2017)

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Parseval Networks: Improving Robustness to Adversarial Examples., , , , and . ICML, volume 70 of Proceedings of Machine Learning Research, page 854-863. PMLR, (2017)Unsupervised Dense Information Retrieval with Contrastive Learning., , , , , , and . Trans. Mach. Learn. Res., (2022)XCiT: Cross-Covariance Image Transformers., , , , , , , , , and 1 other author(s). NeurIPS, page 20014-20027. (2021)XCiT: Cross-Covariance Image Transformers., , , , , , , , , and 1 other author(s). CoRR, (2021)Misspelling Oblivious Word Embeddings., , , , , and . NAACL-HLT (1), page 3226-3234. Association for Computational Linguistics, (2019)Co-training 2L Submodels for Visual Recognition., , , , , and . CVPR, page 11701-11710. IEEE, (2023)You Don't Need Data-Augmentation in Self-Supervised Learning., , , , and . CoRR, (2024)Sub-meter resolution canopy height maps using self-supervised learning and a vision transformer trained on Aerial and GEDI Lidar., , , , , , , , , and 6 other author(s). CoRR, (2023)DINOv2: Learning Robust Visual Features without Supervision., , , , , , , , , and 16 other author(s). Trans. Mach. Learn. Res., (2024)Leveraging Large-Scale Uncurated Data for Unsupervised Pre-training of Visual Features., , , and . CoRR, (2019)