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

Recently Added Most Hyped Most Active Needs Verification Most Verified

Showing 4541–4550 of 6661 papers

Title	Date	Tasks	Status	Hype
Improving Self-Supervised Learning by Characterizing Idealized Representations	Sep 13, 2022	Contrastive LearningSelf-Supervised Learning	CodeCode Available	1
Multi-stage Distillation Framework for Cross-Lingual Semantic Similarity Matching	Sep 13, 2022	Contrastive LearningKnowledge Distillation	CodeCode Available	0
Don't Judge a Language Model by Its Last Layer: Contrastive Learning with Layer-Wise Attention Pooling	Sep 13, 2022	Contrastive LearningLanguage Modeling	CodeCode Available	0
A Molecular Multimodal Foundation Model Associating Molecule Graphs with Natural Language	Sep 12, 2022	Contrastive LearningCross-Modal Retrieval	CodeCode Available	1
Robust Category-Level 6D Pose Estimation with Coarse-to-Fine Rendering of Neural Features	Sep 12, 2022	6D Pose EstimationContrastive Learning	CodeCode Available	0
SANCL: Multimodal Review Helpfulness Prediction with Selective Attention and Natural Contrastive Learning	Sep 12, 2022	Contrastive Learning	CodeCode Available	0
Hyperbolic Self-supervised Contrastive Learning Based Network Anomaly Detection	Sep 12, 2022	Anomaly DetectionContrastive Learning	—Unverified	0
Ranking-Enhanced Unsupervised Sentence Representation Learning	Sep 9, 2022	Contrastive LearningData Augmentation	CodeCode Available	1
Information Maximization for Extreme Pose Face Recognition	Sep 7, 2022	Contrastive LearningDomain Adaptation	—Unverified	0
Semi-supervised Crowd Counting via Density Agency	Sep 7, 2022	Contrastive LearningCrowd Counting	CodeCode Available	1

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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