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 4691–4700 of 6661 papers

Title	Date	Tasks	Status	Hype
Deep Pneumonia: Attention-Based Contrastive Learning for Class-Imbalanced Pneumonia Lesion Recognition in Chest X-rays	Jul 23, 2022	Contrastive LearningHard Attention	—Unverified	0
Self-supervised contrastive learning of echocardiogram videos enables label-efficient cardiac disease diagnosis	Jul 23, 2022	ClassificationContrastive Learning	CodeCode Available	1
Online Knowledge Distillation via Mutual Contrastive Learning for Visual Recognition	Jul 23, 2022	Contrastive Learningimage-classification	CodeCode Available	1
Bi-directional Contrastive Learning for Domain Adaptive Semantic Segmentation	Jul 22, 2022	Contrastive LearningDomain Adaptation	—Unverified	0
Adaptive Soft Contrastive Learning	Jul 22, 2022	Contrastive LearningRepresentation Learning	CodeCode Available	1
Decoupled Adversarial Contrastive Learning for Self-supervised Adversarial Robustness	Jul 22, 2022	Adversarial RobustnessContrastive Learning	CodeCode Available	1
In Defense of Online Models for Video Instance Segmentation	Jul 21, 2022	Contrastive LearningInstance Segmentation	CodeCode Available	2
Correspondence Matters for Video Referring Expression Comprehension	Jul 21, 2022	Contrastive LearningReferring Expression	CodeCode Available	1
Feature Representation Learning for Unsupervised Cross-domain Image Retrieval	Jul 20, 2022	Contrastive LearningImage Retrieval	CodeCode Available	1
Hierarchically Self-Supervised Transformer for Human Skeleton Representation Learning	Jul 20, 2022	Action DetectionAction Recognition	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