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 3781–3790 of 6661 papers

Title	Date	Tasks	Status	Hype
Learning Representation for Anomaly Detection of Vehicle Trajectories	Mar 9, 2023	Anomaly DetectionAutonomous Driving	—Unverified	0
An Evaluation of Non-Contrastive Self-Supervised Learning for Federated Medical Image Analysis	Mar 9, 2023	Contrastive LearningFederated Learning	—Unverified	0
Mimic before Reconstruct: Enhancing Masked Autoencoders with Feature Mimicking	Mar 9, 2023	Contrastive LearningDecoder	CodeCode Available	2
Contrastive Model Adaptation for Cross-Condition Robustness in Semantic Segmentation	Mar 9, 2023	Contrastive LearningDomain Adaptation	CodeCode Available	1
ESCL: Equivariant Self-Contrastive Learning for Sentence Representations	Mar 9, 2023	Contrastive LearningMulti-Task Learning	—Unverified	0
Structure-Aware Group Discrimination with Adaptive-View Graph Encoder: A Fast Graph Contrastive Learning Framework	Mar 9, 2023	Contrastive LearningGraph Representation Learning	—Unverified	0
Adversarial Modality Alignment Network for Cross-Modal Molecule Retrieval	Mar 8, 2023	Contrastive LearningCross-Modal Retrieval	CodeCode Available	0
Semantically Consistent Multi-view Representation Learning	Mar 8, 2023	Contrastive LearningRepresentation Learning	—Unverified	0
A Message Passing Perspective on Learning Dynamics of Contrastive Learning	Mar 8, 2023	Contrastive LearningGraph Attention	CodeCode Available	1
Sample-efficient Real-time Planning with Curiosity Cross-Entropy Method and Contrastive Learning	Mar 7, 2023	continuous-controlContinuous Control	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