Results 51 to 60 of about 26,557 (311)
Towards Domain-Agnostic Contrastive Learning [PDF]
, 2021 Despite recent successes, most contrastive self-supervised learning methods are domain-specific, relying heavily on data augmentation techniques that require knowledge about a particular domain, such as image cropping and rotation.
Kawaguchi, Kenji +4 more
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SSCLNet: A Self-Supervised Contrastive Loss-Based Pre-Trained Network for Brain MRI Classification
IEEE Access, 2023 Brain magnetic resonance images (MRI) convey vital information for making diagnostic decisions and are widely used to detect brain tumors. This research proposes a self-supervised pre-training method based on feature representation learning through ...
Animesh Mishra +2 more
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Faithful Contrastive Features in Learning [PDF]
Cognitive Science, 2006 AbstractThis article pursues the idea of inferring aspects of phonological underlying forms directly from surface contrasts by looking at optimality theoretic linguistic systems (Prince & Smolensky, 1993/2004). The main result proves that linguistic systems satisfying certain conditions have the faithful contrastive feature property: Whenever 2 ...
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Spectral Temporal Contrastive Learning
CoRR, 2023 Accepted to Self-Supervised Learning - Theory and Practice, NeurIPS Workshop ...
Sacha Morin +3 more
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MathematicsGraph contrastive learning has demonstrated significant superiority for collaborative filtering. These methods typically use augmentation technology to generate contrastive views, and then train graph neural networks with contrastive learning as an ...
Jifeng Dong +5 more
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Conditional Contrastive Learning with Kernel
CoRR, 2022 Conditional contrastive learning frameworks consider the conditional sampling procedure that constructs positive or negative data pairs conditioned on specific variables. Fair contrastive learning constructs negative pairs, for example, from the same gender (conditioning on sensitive information), which in turn reduces undesirable information from the ...
Yao-Hung Hubert Tsai +6 more
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Generalization Analysis for Contrastive Representation Learning [PDF]
, 2023 Recently, contrastive learning has found impressive success in advancing the state of the art in solving various machine learning tasks. However, the existing generalization analysis is very limited or even not meaningful.
Yang, Tianbao +3 more
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CC-GNN: A Clustering Contrastive Learning Network for Graph Semi-Supervised Learning
IEEE AccessIn graph modeling, scarcity of labeled data is a challenging issue. To address this issue, state-of-the-art graph models learn the representation of graph data via contrastive learning.
Peng Qin +4 more
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Robust age estimation model using group‐aware contrastive learning
IET Image Processing, 2022 Although great efforts have been devoted to developing lightweight models for age estimation in recent works, the robustness is still unsatisfactory in unconstrained environments.
Xiaoqiang Li +4 more
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Contrastive Learning for Depth Prediction
2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), 2023 Depth prediction is at the core of several computer vision applications, such as autonomous driving and robotics. It is often formulated as a regression task in which depth values are estimated through network layers. Unfortunately, the distribution of values on depth maps is seldom explored.
Fan, Rizhao +2 more
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