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 3851–3860 of 6661 papers

Title	Date	Tasks	Status	Hype
A Global and Patch-wise Contrastive Loss for Accurate Automated Exudate Detection	Feb 22, 2023	Contrastive LearningSegmentation	CodeCode Available	0
Few-Shot Point Cloud Semantic Segmentation via Contrastive Self-Supervision and Multi-Resolution Attention	Feb 21, 2023	Contrastive LearningFew-Shot Learning	—Unverified	0
Unpaired Translation from Semantic Label Maps to Images by Leveraging Domain-Specific Simulations	Feb 21, 2023	Contrastive LearningImage Generation	—Unverified	0
A General-Purpose Transferable Predictor for Neural Architecture Search	Feb 21, 2023	Contrastive LearningGraph Representation Learning	—Unverified	0
Multi-Modal Self-Supervised Learning for Recommendation	Feb 21, 2023	Contrastive LearningData Augmentation	CodeCode Available	2
Mask-guided BERT for Few Shot Text Classification	Feb 21, 2023	ClassificationContrastive Learning	—Unverified	0
Few-shot Detection of Anomalies in Industrial Cyber-Physical System via Prototypical Network and Contrastive Learning	Feb 21, 2023	Anomaly DetectionContrastive Learning	—Unverified	0
DrasCLR: A Self-supervised Framework of Learning Disease-related and Anatomy-specific Representation for 3D Medical Images	Feb 21, 2023	AnatomyContrastive Learning	—Unverified	0
Generalization Bounds for Adversarial Contrastive Learning	Feb 21, 2023	Contrastive LearningGeneralization Bounds	—Unverified	0
Heterogeneous Social Event Detection via Hyperbolic Graph Representations	Feb 20, 2023	Contrastive LearningEvent Detection	CodeCode Available	0

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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