Results 41 to 50 of about 26,557 (311)
Learning Representations with Contrastive Self-Supervised Learning for Histopathology Applications [PDF]
, 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
core
MoCoUTRL: a momentum contrastive framework for unsupervised text representation learning
Connection Science, 2023 This paper presents MoCoUTRL: a Momentum Contrastive Framework for Unsupervised Text Representation Learning. This model improves two aspects of recently popular contrastive learning algorithms in natural language processing (NLP).
Ao Zou +4 more
doaj +1 more source
Multi-Modal 3D Shape Clustering with Dual Contrastive Learning
Applied Sciences, 2022 3D shape clustering is developing into an important research subject with the wide applications of 3D shapes in computer vision and multimedia fields. Since 3D shapes generally take on various modalities, how to comprehensively exploit the multi-modal ...
Guoting Lin +4 more
doaj +1 more source
Frontiers in Physics, 2023 An improved algorithm has been proposed to address the challenges encountered in object detection using visible and thermal infrared images. These challenges include the diversity of object detection perspectives, deformation of the object, occlusion ...
Xiaoguang Tu +7 more
doaj +1 more source
Contrastive Learning for Fair Representations
CoRR, 2021 Trained classification models can unintentionally lead to biased representations and predictions, which can reinforce societal preconceptions and stereotypes. Existing debiasing methods for classification models, such as adversarial training, are often expensive to train and difficult to optimise.
Aili Shen +4 more
openaire +2 more sources
Supervised contrastive learning for recommendation
Knowledge-Based Systems, 2022 In this work, we aim to consider the application of contrastive learning in the scenario of the recommendation system adequately, making it more suitable for recommendation task. We propose a learning paradigm called supervised contrastive learning(SCL) to support the graph convolutional neural network.
Chun Yang +4 more
openaire +3 more sources
Self-Supervised Sequence Recommendation Method Based on Random Self-Attention and Momentum Contrastive Learning [PDF]
Jisuanji gongchengSequence recommendation utilizes user historical sequence behavior to model user interests and provide content recommendations, and is commonly employed in sectors such as news, advertising, and e-commerce.
YU Zhengtao, SUN Ziqin, ZHANG Yongbing, GAO Shengxiang, HUANG Yuxin, TAN Kaiwen
doaj +1 more source
Unifying Graph Contrastive Learning with Flexible Contextual Scopes [PDF]
, 2022 Graph contrastive learning (GCL) has recently emerged as an effective learning paradigm to alleviate the reliance on labelling information for graph representation learning. The core of GCL is to maximise the mutual information between the representation
Zhou, X +4 more
core +1 more source
Unbiased Supervised Contrastive Learning
CoRR, 2022 Accepted at ICLR 2023 (v3); Fix typo in Eq.19 (v4)
Carlo Alberto Barbano +4 more
openaire +4 more sources