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 4411–4420 of 6661 papers

Title	Date	Tasks	Status	Hype
Robust Diversified Graph Contrastive Network for Incomplete Multi-view Clustering	Oct 10, 2022	ClusteringContrastive Learning	CodeCode Available	0
Multilingual Representation Distillation with Contrastive Learning	Oct 10, 2022	Contrastive LearningCross-Lingual Information Retrieval	—Unverified	0
Improving Continual Relation Extraction through Prototypical Contrastive Learning	Oct 10, 2022	Continual Relation ExtractionContrastive Learning	—Unverified	0
Using Whole Slide Image Representations from Self-Supervised Contrastive Learning for Melanoma Concordance Regression	Oct 10, 2022	Contrastive LearningDecision Making	—Unverified	0
Contrastive Bayesian Analysis for Deep Metric Learning	Oct 10, 2022	Contrastive LearningMetric Learning	CodeCode Available	1
SMiLE: Schema-augmented Multi-level Contrastive Learning for Knowledge Graph Link Prediction	Oct 10, 2022	Contrastive LearningKnowledge Graphs	CodeCode Available	1
Learning "O" Helps for Learning More: Handling the Concealed Entity Problem for Class-incremental NER	Oct 10, 2022	class-incremental learningClass Incremental Learning	—Unverified	0
Towards Robust Visual Question Answering: Making the Most of Biased Samples via Contrastive Learning	Oct 10, 2022	Contrastive LearningQuestion Answering	CodeCode Available	1
CONSS: Contrastive Learning Approach for Semi-Supervised Seismic Facies Classification	Oct 10, 2022	Contrastive LearningFacies Classification	—Unverified	0
HiCo: Hierarchical Contrastive Learning for Ultrasound Video Model Pretraining	Oct 10, 2022	Contrastive Learning	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