Results 31 to 40 of about 26,557 (311)
Contrastive Attraction and Contrastive Repulsion for Representation Learning
Trans. Mach. Learn. Res., 2021 Contrastive learning (CL) methods effectively learn data representations in a self-supervision manner, where the encoder contrasts each positive sample over multiple negative samples via a one-vs-many softmax cross-entropy loss. By leveraging large amounts of unlabeled image data, recent CL methods have achieved promising results when pretrained on ...
Huangjie Zheng +9 more
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Neighbor Contrastive Learning on Learnable Graph Augmentation [PDF]
, 2023 Recent years, graph contrastive learning (GCL), which aims to learn representations from unlabeled graphs, has made great progress. However, the existing GCL methods mostly adopt human-designed graph augmentations, which are sensitive to various graph ...
Sun, D +4 more
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Signal Contrastive Enhanced Graph Collaborative Filtering for Recommendation
Data Science and Engineering, 2023 Graph collaborative filtering methods have shown great performance improvements compared with deep neural network-based models. However, these methods suffer from data sparsity and data noise problems.
Zhi-Yuan Li +3 more
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Self-supervised Dynamic Graph Representation Learning Approach Based on Contrastive Prediction [PDF]
Jisuanji kexue, 2023 In recent years,graph self-supervised learning represented by graph contrastive learning has become a hot research to-pic in the field of graph learning.This learning paradigm does not depend on node labels and has good generalization ability.However ...
JIANG Linpu, CHEN Kejia
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A Framework Using Contrastive Learning for Classification with Noisy Labels
Data, 2021 We propose a framework using contrastive learning as a pre-training task to perform image classification in the presence of noisy labels. Recent strategies, such as pseudo-labeling, sample selection with Gaussian Mixture models, and weighted supervised ...
Madalina Ciortan +2 more
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Weakly Supervised Contrastive Learning [PDF]
2021 IEEE/CVF International Conference on Computer Vision (ICCV), 2021 Unsupervised visual representation learning has gained much attention from the computer vision community because of the recent achievement of contrastive learning. Most of the existing contrastive learning frameworks adopt the instance discrimination as the pretext task, which treating every single instance as a different class.
Mingkai Zheng +6 more
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Projective vs. interpretational properties of nuclear accents and the phonology of contrastive focus in Greek [PDF]
, 2010 Georgakopoulos T, Skopeteas S. Projective vs. interpretational properties of nuclear accents and the phonology of contrastive focus in Greek. Linguistic Review. 2010;27(3):319-346.Nuclear accents have two interesting properties.
Georgakopoulos, Thanasis +4 more
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IEEE Access, 2023 Self-supervised learning has been shown to be effective in various fields, proving its usefulness in contrastive learning. Recently, graph contrastive learning has shown state-of-the-art performance in the recommendation task.
Sanghun Kim, Hyeryung Jang
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SC-FGCL: Self-Adaptive Cluster-Based Federal Graph Contrastive Learning
IEEE Open Journal of the Computer Society, 2023 As a self-supervised learning method, the graph contrastive learning achieve admirable performance in graph pre-training tasks, and can be fine-tuned for multiple downstream tasks such as protein structure prediction, social recommendation, etc.
Tingqi Wang +4 more
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Poisoning and Backdooring Contrastive Learning
CoRR, 2021 Multimodal contrastive learning methods like CLIP train on noisy and uncurated training datasets. This is cheaper than labeling datasets manually, and even improves out-of-distribution robustness. We show that this practice makes backdoor and poisoning attacks a significant threat.
Nicholas Carlini, Andreas Terzis
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