Results 11 to 20 of about 5,885,991 (322)
FreeMatch: Self-adaptive Thresholding for Semi-supervised Learning [PDF]
International Conference on Learning Representations, 2022 Semi-supervised Learning (SSL) has witnessed great success owing to the impressive performances brought by various methods based on pseudo labeling and consistency regularization.
Yidong Wang +8 more
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A Cookbook of Self-Supervised Learning [PDF]
arXiv.org, 2023 Self-supervised learning, dubbed the dark matter of intelligence, is a promising path to advance machine learning. Yet, much like cooking, training SSL methods is a delicate art with a high barrier to entry.
Randall Balestriero +18 more
semanticscholar +1 more source
Self-Supervised Transfer Learning from Natural Images for Sound Classification
Applied Sciences, 2021 We propose the implementation of transfer learning from natural images to audio-based images using self-supervised learning schemes. Through self-supervised learning, convolutional neural networks (CNNs) can learn the general representation of natural ...
Sungho Shin +4 more
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Self-Supervised Learning: Generative or Contrastive [PDF]
IEEE Transactions on Knowledge and Data Engineering, 2020 Deep supervised learning has achieved great success in the last decade. However, its defects of heavy dependence on manual labels and vulnerability to attacks have driven people to find other paradigms.
Xiao Liu +6 more
semanticscholar +1 more source
A Survey on Self-Supervised Learning: Algorithms, Applications, and Future Trends [PDF]
IEEE Transactions on Pattern Analysis and Machine Intelligence, 2023 Deep supervised learning algorithms typically require a large volume of labeled data to achieve satisfactory performance. However, the process of collecting and labeling such data can be expensive and time-consuming.
Jie Gui +6 more
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Multi-Modal Self-Supervised Learning for Recommendation [PDF]
The Web Conference, 2023 The online emergence of multi-modal sharing platforms (e.g., TikTok, Youtube) is powering personalized recommender systems to incorporate various modalities (e.g., visual, textual and acoustic) into the latent user representations.
Wei Wei +3 more
semanticscholar +1 more source
Europhysics Letters, 2023 Abstract In neural network's literature, Hebbian learning traditionally refers to the procedure by which the Hopfield model and its generalizations store archetypes (i.e., definite patterns that are experienced just once to form the synaptic matrix).
Francesco Alemanno +4 more
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IEEE Access, 2023 Despite the remarkable progress of self-supervised learning (SSL), how self-supervised representations generalize to out-of-distribution data remains little understood.
Samira Zare, Hien Van Nguyen
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A Survey on Contrastive Self-supervised Learning [PDF]
Technologies, 2020 Self-supervised learning has gained popularity because of its ability to avoid the cost of annotating large-scale datasets. It is capable of adopting self-defined pseudolabels as supervision and use the learned representations for several downstream ...
Ashish Jaiswal +4 more
semanticscholar +1 more source
Deeper Insights into Graph Convolutional Networks for Semi-Supervised Learning [PDF]
AAAI Conference on Artificial Intelligence, 2018 Many interesting problems in machine learning are being revisited with new deep learning tools. For graph-based semi-supervised learning, a recent important development is graph convolutional networks (GCNs), which nicely integrate local vertex ...
Qimai Li, Zhichao Han, Xiao-Ming Wu
semanticscholar +1 more source