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 3861–3870 of 6661 papers

Title	Date	Tasks	Status	Hype
Pseudo Contrastive Learning for Graph-based Semi-supervised Learning	Feb 19, 2023	Contrastive LearningData Augmentation	—Unverified	0
Supervised Contrastive Learning and Feature Fusion for Improved Kinship Verification	Feb 19, 2023	Contrastive LearningKinship Verification	—Unverified	0
Bag of Tricks for Effective Language Model Pretraining and Downstream Adaptation: A Case Study on GLUE	Feb 18, 2023	Contrastive LearningDenoising	—Unverified	0
Data-Efficient Contrastive Self-supervised Learning: Most Beneficial Examples for Supervised Learning Contribute the Least	Feb 18, 2023	Contrastive LearningOpen-Ended Question Answering	CodeCode Available	1
EnfoMax: Domain Entropy and Mutual Information Maximization for Domain Generalized Face Anti-spoofing	Feb 17, 2023	Binary ClassificationContrastive Learning	—Unverified	0
Building Shortcuts between Distant Nodes with Biaffine Mapping for Graph Convolutional Networks	Feb 17, 2023	Contrastive LearningNode Classification	—Unverified	0
Like a Good Nearest Neighbor: Practical Content Moderation and Text Classification	Feb 17, 2023	ClassificationContrastive Learning	CodeCode Available	1
Self-supervised Action Representation Learning from Partial Spatio-Temporal Skeleton Sequences	Feb 17, 2023	Action RecognitionContrastive Learning	CodeCode Available	1
Bridge the Gap between Language models and Tabular Understanding	Feb 16, 2023	Contrastive LearningLanguage Modeling	—Unverified	0
LightGCL: Simple Yet Effective Graph Contrastive Learning for Recommendation	Feb 16, 2023	Contrastive LearningData Augmentation	CodeCode Available	2

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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