Results 11 to 20 of about 120,140 (335)
Contrastive Representation Learning: A Framework and Review
IEEE Access, 2020 Contrastive Learning has recently received interest due to its success in self-supervised representation learning in the computer vision domain. However, the origins of Contrastive Learning date as far back as the 1990s and its development has spanned ...
Phuc H. Le-Khac +2 more
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Contrastive Learning with Stronger Augmentations
IEEE Transactions on Pattern Analysis and Machine Intelligence, 2022 12 pages, 6 ...
Xiao Wang, Guo-Jun Qi
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Parametric Contrastive Learning [PDF]
2021 IEEE/CVF International Conference on Computer Vision (ICCV), 2021 In this paper, we propose Parametric Contrastive Learning (PaCo) to tackle long-tailed recognition. Based on theoretical analysis, we observe supervised contrastive loss tends to bias on high-frequency classes and thus increases the difficulty of imbalanced learning.
Jiequan Cui +4 more
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Contrasting the landscape of contrastive and non-contrastive learning
CoRR, 2022 A lot of recent advances in unsupervised feature learning are based on designing features which are invariant under semantic data augmentations. A common way to do this is contrastive learning, which uses positive and negative samples. Some recent works however have shown promising results for non-contrastive learning, which does not require negative ...
Ashwini Pokle +3 more
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Hyperbolic Contrastive Learning
CoRR, 2023 Learning good image representations that are beneficial to downstream tasks is a challenging task in computer vision. As such, a wide variety of self-supervised learning approaches have been proposed. Among them, contrastive learning has shown competitive performance on several benchmark datasets.
Yun Yue +3 more
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Applied Sciences, 2023 Font is a crucial design aspect, however, classifying fonts is challenging compared with that of other natural objects, as fonts differ from images. This paper presents the application of contrastive learning in font style classification.
Irfanullah Memon +2 more
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Stochastic Contrastive Learning
CoRR, 2021 While state-of-the-art contrastive Self-Supervised Learning (SSL) models produce results competitive with their supervised counterparts, they lack the ability to infer latent variables. In contrast, prescribed latent variable (LV) models enable attributing uncertainty, inducing task specific compression, and in general allow for more interpretable ...
Jason Ramapuram +3 more
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Clustering of Short Texts Based on Dynamic Adjustment for Contrastive Learning
IEEE Access, 2022 Faced with the large amount of unlabeled short text data appearing on the Internet, it is necessary to categorize them using clustering that can divide text into several clusters based on similarity degree of text semantics.
Ruihui Li, Hongbin Wang
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CL-TAD: A Contrastive-Learning-Based Method for Time Series Anomaly Detection
Applied Sciences, 2023 Anomaly detection has gained increasing attention in recent years, but detecting anomalies in time series data remains challenging due to temporal dynamics, label scarcity, and data diversity in real-world applications.
Huynh Cong Viet Ngu, Keon Myung Lee
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Heterogeneous Contrastive Learning
CoRR, 2021 With the advent of big data across multiple high-impact applications, we are often facing the challenge of complex heterogeneity. The newly collected data usually consist of multiple modalities and are characterized with multiple labels, thus exhibiting the co-existence of multiple types of heterogeneity.
Lecheng Zheng +3 more
openaire +2 more sources