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Graph Self-Supervised Learning: A Survey [PDF]
, 2022 Deep learning on graphs has attracted significant interests recently. However, most of the works have focused on (semi-) supervised learning, resulting in shortcomings including heavy label reliance, poor generalization, and weak robustness.
Liu, Y +21 more
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Self-Supervised Node Classification with Strategy and Actively Selected Labeled Set
Entropy, 2022 To alleviate the impact of insufficient labels in less-labeled classification problems, self-supervised learning improves the performance of graph neural networks (GNNs) by focusing on the information of unlabeled nodes.
Yi Kang +3 more
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Boosting Self-Supervised Learning via Knowledge Transfer [PDF]
, 2018 In self-supervised learning, one trains a model to solve a so-called pretext task on a dataset without the need for human annotation. The main objective, however, is to transfer this model to a target domain and task.
Ananth Vinjimoor +7 more
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Supervised learning of arithmetic invariants
, 2023 We survey some recent implementations of supervised learning techniques on large sets of arithmetic data. As part of our methodological review, we perform some rudimentary statistical learning algorithms by hand on simplified problems.
Oliver, T.
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Review of Self-supervised Learning Methods in Field of ECG [PDF]
Jisuanji kexue yu tansuoDeep learning has been widely applied in the field of electrocardiogram (ECG) signal analysis due to its powerful data representation capability. However, supervised methods require a large amount of labeled data, and ECG data annotation is typically ...
HAN Han, HUANG Xunhua, CHANG Huihui, FAN Haoyi, CHEN Peng, CHEN Jijia
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Comparing Learning Methodologies for Self-Supervised Audio-Visual Representation Learning
IEEE Access, 2022 In recent years, the machine learning community has devoted an increasing attention to self-supervised learning.The performance gap between supervised and self-supervised has become increasingly narrow in many computer vision applications. In this paper,
Hacene Terbouche +3 more
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Blended learning environments to foster self-directed learning [PDF]
, 2022 This book on blended learning environments to foster self-directed learning highlights the focus on research conducted in several teaching and learning contexts where blended learning had been implemented and focused on the fostering of self-directed ...
Ngwenya, Emmanuel +16 more
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Learning Representations with Contrastive Self-Supervised Learning for Histopathology Applications
, 2022 Unsupervised learning has made substantial progress over the last few years, especially by means of contrastive self-supervised learning. The dominating dataset for benchmarking self-supervised learning has been ImageNet, for which recent methods are ...
Eilertsen, Gabriel +3 more
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Reduce the Difficulty of Incremental Learning With Self-Supervised Learning
IEEE Access, 2021 Incremental learning requires a learning model to learn new tasks without forgetting the learned tasks continuously. However, when a deep learning model learns new tasks, it will catastrophically forget tasks it has learned before.
Linting Guan, Yan Wu
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Self-Supervised Learning :Video Clip Order Prediction with diffusion models [PDF]
, 2023 openIn recent years, neural networks have achieved incredible performance in computer vision applications like image classification and video identification due to the availability of powerful computational resources and vast amounts of data.
REPETTO, SARA
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