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NeurIPS 2020 Workshop Proposal Self-Supervised Learning: Theory and Practice
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NeurIPS 2021 Workshop: Self-Supervised Learning - Theory and Practice NeurIPS 2021 Workshop: Self-Supervised Learning - Theory and Practice Home Call for Submissions Schedule Speakers Organizers Program Committee Accepted Papers Online Workshop, 07:00 AM – 04:30 PM (PST), Dec 14, 2021 Workshop webpage: https://neurips.cc/Conferences/2021/Schedule?showEvent=21853 Conference (NeurIPS registered users): https://neurips.cc/virtual/2021/workshop/21853 For poster sessions: posters can be viewed in the Accepted Papers page. If interested in a poster, you can click on the corresponding Zoom link. The authors will be presenting their posters in their Zoom. Self-supervised learning (SSL) is an unsupervised approach for representation learning without relying on human-provided labels. It creates auxiliary tasks on unlabeled input data and learns representations by solving these tasks. SSL has demonstrated great success on images (e.g., MoCo, PIRL, SimCLR)...
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