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 1381–1390 of 6661 papers

Title	Date	Tasks	Status	Hype
Improving Molecular Contrastive Learning via Faulty Negative Mitigation and Decomposed Fragment Contrast	Feb 18, 2022	Computational chemistryContrastive Learning	CodeCode Available	1
Eliciting Structural and Semantic Global Knowledge in Unsupervised Graph Contrastive Learning	Feb 17, 2022	Contrastive LearningGraph Neural Network	CodeCode Available	1
Contrastive Meta Learning with Behavior Multiplicity for Recommendation	Feb 17, 2022	Contrastive LearningMeta-Learning	CodeCode Available	1
Neighborhood Contrastive Learning for Scientific Document Representations with Citation Embeddings	Feb 14, 2022	Citation PredictionContrastive Learning	CodeCode Available	1
Learning Weakly-Supervised Contrastive Representations	Feb 14, 2022	Contrastive LearningRepresentation Learning	CodeCode Available	1
Improving Graph Collaborative Filtering with Neighborhood-enriched Contrastive Learning	Feb 13, 2022	Collaborative FilteringContrastive Learning	CodeCode Available	1
What Makes Good Contrastive Learning on Small-Scale Wearable-based Tasks?	Feb 12, 2022	Activity RecognitionContrastive Learning	CodeCode Available	1
Metadata-Induced Contrastive Learning for Zero-Shot Multi-Label Text Classification	Feb 11, 2022	Contrastive LearningMulti Label Text Classification	CodeCode Available	1
Conditional Contrastive Learning with Kernel	Feb 11, 2022	Contrastive Learning	CodeCode Available	1
Energy-Based Contrastive Learning of Visual Representations	Feb 10, 2022	Contrastive LearningSelf-Supervised 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