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 791–800 of 6661 papers

Title	Date	Tasks	Status	Hype
CETN: Contrast-enhanced Through Network for CTR Prediction	Dec 15, 2023	Click-Through Rate PredictionContrastive Learning	CodeCode Available	1
ArtNeRF: A Stylized Neural Field for 3D-Aware Cartoonized Face Synthesis	Apr 21, 2024	3D-Aware Image SynthesisContrastive Learning	CodeCode Available	1
Adversarial Training of Self-supervised Monocular Depth Estimation against Physical-World Attacks	Jan 31, 2023	Adversarial RobustnessAutonomous Driving	CodeCode Available	1
EViT: Privacy-Preserving Image Retrieval via Encrypted Vision Transformer in Cloud Computing	Aug 31, 2022	Cloud ComputingContrastive Learning	CodeCode Available	1
Contrastive Learning of Musical Representations	Mar 17, 2021	Contrastive LearningLinear evaluation	CodeCode Available	1
Expectation-Maximization Contrastive Learning for Compact Video-and-Language Representations	Nov 21, 2022	Contrastive LearningRepresentation Learning	CodeCode Available	1
Explanation Graph Generation via Pre-trained Language Models: An Empirical Study with Contrastive Learning	Apr 11, 2022	Contrastive LearningGraph Generation	CodeCode Available	1
Explanation Guided Contrastive Learning for Sequential Recommendation	Sep 3, 2022	Contrastive LearningRepresentation Learning	CodeCode Available	1
Contrastive learning of global and local features for medical image segmentation with limited annotations	Jun 18, 2020	Contrastive LearningData Augmentation	CodeCode Available	1
A Broad Study on the Transferability of Visual Representations with Contrastive Learning	Mar 24, 2021	Contrastive LearningLinear evaluation	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