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 1881–1890 of 6661 papers

Title	Date	Tasks	Status	Hype
Unmasking Anomalies in Road-Scene Segmentation	Jul 25, 2023	Anomaly DetectionAnomaly Segmentation	CodeCode Available	1
Unraveling the "Anomaly" in Time Series Anomaly Detection: A Self-supervised Tri-domain Solution	Nov 19, 2023	Anomaly DetectionContrastive Learning	CodeCode Available	1
Unsupervised Contrastive Learning of Image Representations from Ultrasound Videos with Hard Negative Mining	Jul 26, 2022	Contrastive Learning	CodeCode Available	1
Extending global-local view alignment for self-supervised learning with remote sensing imagery	Mar 12, 2023	Change DetectionContrastive Learning	CodeCode Available	1
Directed Graph Contrastive Learning	Dec 1, 2021	Contrastive LearningData Augmentation	CodeCode Available	1
Unsupervised Dense Retrieval with Relevance-Aware Contrastive Pre-Training	Jun 5, 2023	Contrastive LearningRetrieval	CodeCode Available	1
Combined Scaling for Zero-shot Transfer Learning	Nov 19, 2021	ClassificationContrastive Learning	—Unverified	0
Combating the Bucket Effect:Multi-Knowledge Alignment for Medication Recommendation	Apr 25, 2025	Contrastive Learning	—Unverified	0
Unified Framework for Feature Extraction based on Contrastive Learning	Jan 25, 2021	Contrastive LearningGraph Embedding	—Unverified	0
A Unified Framework for Contrastive Learning from a Perspective of Affinity Matrix	Nov 26, 2022	Contrastive LearningRepresentation 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