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 1081–1090 of 6661 papers

Title	Date	Tasks	Status	Hype
Towards Effective Image Manipulation Detection with Proposal Contrastive Learning	Oct 16, 2022	Contrastive LearningImage Manipulation	CodeCode Available	1
How Mask Matters: Towards Theoretical Understandings of Masked Autoencoders	Oct 15, 2022	Contrastive LearningSelf-Supervised Learning	CodeCode Available	1
Augmented Dual-Contrastive Aggregation Learning for Unsupervised Visible-Infrared Person Re-Identification	Oct 14, 2022	Contrastive LearningPerson Re-Identification	CodeCode Available	1
MICO: A Multi-alternative Contrastive Learning Framework for Commonsense Knowledge Representation	Oct 14, 2022	Contrastive LearningKnowledge Graph Completion	CodeCode Available	1
Fine-grained Category Discovery under Coarse-grained supervision with Hierarchical Weighted Self-contrastive Learning	Oct 14, 2022	Contrastive Learning	CodeCode Available	1
Low-resource Neural Machine Translation with Cross-modal Alignment	Oct 13, 2022	Contrastive Learningcross-modal alignment	CodeCode Available	1
Multi-Granularity Cross-modal Alignment for Generalized Medical Visual Representation Learning	Oct 12, 2022	Contrastive Learningcross-modal alignment	CodeCode Available	1
Self-Attention Message Passing for Contrastive Few-Shot Learning	Oct 12, 2022	Contrastive LearningFew-Shot Learning	CodeCode Available	1
Contrastive Retrospection: honing in on critical steps for rapid learning and generalization in RL	Oct 12, 2022	Contrastive LearningOut-of-Distribution Generalization	CodeCode Available	1
Contrastive Trajectory Similarity Learning with Dual-Feature Attention	Oct 11, 2022	Contrastive LearningTrajectory Modeling	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