Recent advancements in self-supervised paradigms for visual feature representation

Show simple item record Anand, Mrinal Garg, Aditya 2021-11-24T13:31:13Z 2021-11-24T13:31:13Z 2021-11
dc.identifier.citation Anand, Mrinal and Garg, Aditya, "Recent advancements in self-supervised paradigms for visual feature representation", arXiv, Cornell University Library, DOI: arXiv:2111.02042, Nov. 2021 en_US
dc.description.abstract We witnessed a massive growth in the supervised learning paradigm in the past decade. Supervised learning requires a large amount of labeled data to reach state-of-the-art performance. However, labeling the samples requires a lot of human annotation. To avoid the cost of labeling data, self-supervised methods were proposed to make use of largely available unlabeled data. This study conducts a comprehensive and insightful survey and analysis of recent developments in the self-supervised paradigm for feature representation. In this paper, we investigate the factors affecting the usefulness of self-supervision under different settings. We present some of the key insights concerning two different approaches in self-supervision, generative and contrastive methods. We also investigate the limitations of supervised adversarial training and how self-supervision can help overcome those limitations. We then move on to discuss the limitations and challenges in effectively using self-supervision for visual tasks. Finally, we highlight some open problems and point out future research directions
dc.description.statementofresponsibility by Mrinal Anand and Aditya Garg
dc.language.iso en_US en_US
dc.publisher Cornell University Library en_US
dc.subject Computer Vision and Pattern Recognition en_US
dc.subject Machine Learning en_US
dc.subject Artificial Intelligence en_US
dc.title Recent advancements in self-supervised paradigms for visual feature representation en_US
dc.type Pre-Print en_US
dc.relation.journal arXiv

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