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 1501–1510 of 6661 papers

Title	Date	Tasks	Status	Hype
3D Infomax improves GNNs for Molecular Property Prediction	Oct 8, 2021	Contrastive LearningGraph Learning	CodeCode Available	1
Temperature as Uncertainty in Contrastive Learning	Oct 8, 2021	Contrastive LearningLinear evaluation	CodeCode Available	1
SubTab: Subsetting Features of Tabular Data for Self-Supervised Representation Learning	Oct 8, 2021	Collaborative InferenceContrastive Learning	CodeCode Available	1
Phone-to-audio alignment without text: A Semi-supervised Approach	Oct 8, 2021	Contrastive Learning	CodeCode Available	1
Model Adaptation: Historical Contrastive Learning for Unsupervised Domain Adaptation without Source Data	Oct 7, 2021	Contrastive LearningDomain Adaptation	CodeCode Available	1
Bridge to Target Domain by Prototypical Contrastive Learning and Label Confusion: Re-explore Zero-Shot Learning for Slot Filling	Oct 7, 2021	Contrastive Learningslot-filling	CodeCode Available	1
KNN-BERT: Fine-Tuning Pre-Trained Models with KNN Classifier	Oct 6, 2021	Contrastive Learningtext-classification	CodeCode Available	1
The Information Geometry of Unsupervised Reinforcement Learning	Oct 6, 2021	Contrastive Learningreinforcement-learning	CodeCode Available	1
ProGCL: Rethinking Hard Negative Mining in Graph Contrastive Learning	Oct 5, 2021	Contrastive LearningRepresentation Learning	CodeCode Available	1
Consistent Explanations by Contrastive Learning	Oct 1, 2021	Contrastive LearningExplainable artificial intelligence	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