Results 21 to 30 of about 115,093 (312)
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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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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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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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
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Heterogeneous Contrastive Learning
, 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.
Zheng, Lecheng +3 more
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Contrastive Learning for Mitochondria Segmentation [PDF]
2021 43rd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC), 2021 Mitochondria segmentation in electron microscopy images is essential in neuroscience. However, due to the image degradation during the imaging process, the large variety of mitochondrial structures, as well as the presence of noise, artifacts and other sub-cellular structures, mitochondria segmentation is very challenging.
Li, Zhili +3 more
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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
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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
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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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Decoupled Contrastive Learning
, 2022 Contrastive learning (CL) is one of the most successful paradigms for self-supervised learning (SSL). In a principled way, it considers two augmented "views" of the same image as positive to be pulled closer, and all other images as negative to be pushed further apart.
Yeh, Chun-Hsiao +5 more
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