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 1801–1810 of 6661 papers

Title	Date	Tasks	Status	Hype
Harmony: A Joint Self-Supervised and Weakly-Supervised Framework for Learning General Purpose Visual Representations	May 23, 2024	Contrastive LearningInstance Segmentation	CodeCode Available	0
Improving Gloss-free Sign Language Translation by Reducing Representation Density	May 23, 2024	Contrastive LearningGloss-free Sign Language Translation	CodeCode Available	1
Rehearsal-free Federated Domain-incremental Learning	May 22, 2024	Contrastive LearningFederated Learning	—Unverified	0
Enhancing Active Learning for Sentinel 2 Imagery through Contrastive Learning and Uncertainty Estimation	May 22, 2024	Active LearningContrastive Learning	—Unverified	0
TrojanRAG: Retrieval-Augmented Generation Can Be Backdoor Driver in Large Language Models	May 22, 2024	Backdoor AttackContrastive Learning	CodeCode Available	0
NERULA: A Dual-Pathway Self-Supervised Learning Framework for Electrocardiogram Signal Analysis	May 21, 2024	Activity RecognitionContrastive Learning	—Unverified	0
Efficient and Interpretable Information Retrieval for Product Question Answering with Heterogeneous Data	May 21, 2024	Contrastive LearningInformation Retrieval	CodeCode Available	0
Learning Structure and Knowledge Aware Representation with Large Language Models for Concept Recommendation	May 21, 2024	Contrastive LearningKnowledge Tracing	—Unverified	0
A Survey of Deep Learning-based Radiology Report Generation Using Multimodal Data	May 21, 2024	Contrastive LearningKnowledge Base Construction	—Unverified	0
C3L: Content Correlated Vision-Language Instruction Tuning Data Generation via Contrastive Learning	May 21, 2024	Contrastive Learning	—Unverified	0

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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