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 1291–1300 of 6661 papers

Title	Date	Tasks	Status	Hype
On Distribution Shift in Learning-based Bug Detectors	Apr 21, 2022	Contrastive Learning	CodeCode Available	1
PreTraM: Self-Supervised Pre-training via Connecting Trajectory and Map	Apr 21, 2022	Contrastive LearningRepresentation Learning	CodeCode Available	1
Video Moment Retrieval from Text Queries via Single Frame Annotation	Apr 20, 2022	Contrastive LearningMoment Retrieval	CodeCode Available	1
Multi-level Cross-view Contrastive Learning for Knowledge-aware Recommender System	Apr 19, 2022	Contrastive LearningData Augmentation	CodeCode Available	1
Learning to Imagine: Diversify Memory for Incremental Learning using Unlabeled Data	Apr 19, 2022	Contrastive LearningDiversity	CodeCode Available	1
Attributed Graph Clustering with Dual Redundancy Reduction	Apr 18, 2022	ClusteringContrastive Learning	CodeCode Available	1
Detect Rumors in Microblog Posts for Low-Resource Domains via Adversarial Contrastive Learning	Apr 18, 2022	Contrastive Learning	CodeCode Available	1
A Contrastive Cross-Channel Data Augmentation Framework for Aspect-based Sentiment Analysis	Apr 16, 2022	Aspect-Based Sentiment AnalysisAspect-Based Sentiment Analysis (ABSA)	CodeCode Available	1
Contrastive Learning with Hard Negative Entities for Entity Set Expansion	Apr 16, 2022	Contrastive LearningLanguage Modeling	CodeCode Available	1
Perfectly Balanced: Improving Transfer and Robustness of Supervised Contrastive Learning	Apr 15, 2022	Contrastive LearningData Augmentation	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