Contrastive Learning

Contrastive Learning is a deep learning technique for unsupervised representation learning. The goal is to learn a representation of data such that similar instances are close together in the representation space, while dissimilar instances are far apart.

It has been shown to be effective in various computer vision and natural language processing tasks, including image retrieval, zero-shot learning, and cross-modal retrieval. In these tasks, the learned representations can be used as features for downstream tasks such as classification and clustering.

(Image credit: Schroff et al. 2015)

Papers

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Showing 2671–2680 of 6661 papers

Title	Date	Tasks	Status
Contrastive News and Social Media Linking using BERT for Articles and Tweets across Dual Platforms	Dec 11, 2023	ArticlesContrastive Learning	—Unverified
C^2M-DoT: Cross-modal consistent multi-view medical report generation with domain transfer network	Oct 9, 2023	Contrastive LearningMedical Report Generation	—Unverified
A Novel Approach to for Multimodal Emotion Recognition : Multimodal semantic information fusion	Feb 12, 2025	Contrastive LearningEmotion Recognition	—Unverified
I^2MD: 3D Action Representation Learning with Inter- and Intra-modal Mutual Distillation	Oct 24, 2023	Contrastive LearningRepresentation Learning	—Unverified
Interactive Contrastive Learning for Self-supervised Entity Alignment	Jan 17, 2022	Contrastive LearningEntity Alignment	—Unverified
Contrastive Mutual Information Maximization for Binary Neural Networks	Sep 29, 2021	BinarizationContrastive Learning	—Unverified
Contrastive Multi-view Subspace Clustering of Hyperspectral Images based on Graph Convolutional Networks	Dec 11, 2023	ClusteringContrastive Learning	—Unverified
Contrastive Multi-view Framework for Customer Lifetime Value Prediction	Jun 26, 2023	Contrastive LearningMarketing	—Unverified
Bures Joint Distribution Alignment with Dynamic Margin for Unsupervised Domain Adaptation	Mar 14, 2022	Contrastive LearningDomain Adaptation	—Unverified
CSI: Contrastive Data Stratification for Interaction Prediction and its Application to Compound-Protein Interaction Prediction	Nov 18, 2021	Contrastive LearningPrediction	—Unverified

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All datasets imagenet-1k 10,000 People - Human Pose Recognition Data CIFAR-10 STL-10

Benchmark Results

#	Model	Metric	Claimed	Verified	Status
1	ResNet50	ImageNet Top-1 Accuracy	73.6	—	Unverified
2	ResNet50	ImageNet Top-1 Accuracy	73	—	Unverified
3	ResNet50	ImageNet Top-1 Accuracy	71.1	—	Unverified
4	ResNet50	ImageNet Top-1 Accuracy	69.3	—	Unverified
5	ResNet50 (v2)	ImageNet Top-1 Accuracy	67.6	—	Unverified
6	ResNet50 (v2)	ImageNet Top-1 Accuracy	63.8	—	Unverified
7	ResNet50	ImageNet Top-1 Accuracy	63.6	—	Unverified
8	ResNet50	ImageNet Top-1 Accuracy	61.5	—	Unverified
9	ResNet50	ImageNet Top-1 Accuracy	61.5	—	Unverified
10	ResNet50 (4×)	ImageNet Top-1 Accuracy	61.3	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	1	0..5sec	1	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	IPCL (ResNet18)	Accuracy (Top-1)	84.77	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	IPCL (ResNet18)	Accuracy (Top-1)	85.55	—	Unverified