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Self-Supervised Learning for Videos: A Survey
ACM Computing Surveys, 2023 The remarkable success of deep learning in various domains relies on the availability of large-scale annotated datasets. However, obtaining annotations is expensive and requires great effort, which is especially challenging for videos. Moreover, the use of human-generated annotations leads to models with biased learning and poor domain ...
Madeline Schiappa +2 more
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Self-Supervised Learning for Electroencephalography
IEEE Transactions on Neural Networks and Learning SystemsDecades of research have shown machine learning superiority in discovering highly nonlinear patterns embedded in electroencephalography (EEG) records compared with conventional statistical techniques. However, even the most advanced machine learning techniques require relatively large, labeled EEG repositories.
Mohammad Hossein Rafiei +3 more
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Self-Supervised Learning for Recommendation
Proceedings of the 31st ACM International Conference on Information & Knowledge Management, 2022Chao Huang 0001 +4 more
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Self-Supervised Learning for Multimedia Recommendation
IEEE Transactions on Multimedia, 2023Zhulin Tao +6 more
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Self-Supervised Visual Feature Learning With Deep Neural Networks: A Survey
IEEE Transactions on Pattern Analysis and Machine Intelligence, 2021Longlong Jing, Yingli Tian
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Self-supervised Learning: A Succinct Review
Archives of Computational Methods in Engineering, 2023Munish Kumar +2 more
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Self-Supervised Learning for Recommender System
Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval, 2022Chao Huang 0001 +3 more
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Abstract Supervised training requires pairs of target image and associated measurements, which are often difficult to collect. This chapter discusses self-supervised learning approaches based on constructing a self-supervised loss for training a neural network to map a measurement to a clean image.
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