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 2971–2980 of 6661 papers

Title	Date	Tasks	Status	Hype
Domain-Aware Augmentations for Unsupervised Online General Continual Learning	Sep 13, 2023	Continual LearningContrastive Learning	—Unverified	0
Instance Adaptive Prototypical Contrastive Embedding for Generalized Zero Shot Learning	Sep 13, 2023	Contrastive LearningGeneralized Zero-Shot Learning	—Unverified	0
Contrast-Phys+: Unsupervised and Weakly-supervised Video-based Remote Physiological Measurement via Spatiotemporal Contrast	Sep 13, 2023	Computational EfficiencyContrastive Learning	CodeCode Available	1
Multi-behavior Recommendation with SVD Graph Neural Networks	Sep 13, 2023	Contrastive LearningRecommendation Systems	—Unverified	0
Learning from History: Task-agnostic Model Contrastive Learning for Image Restoration	Sep 12, 2023	Contrastive LearningImage Dehazing	CodeCode Available	1
Narrowing the Gap between Supervised and Unsupervised Sentence Representation Learning with Large Language Model	Sep 12, 2023	AttributeContrastive Learning	CodeCode Available	0
Normality Learning-based Graph Anomaly Detection via Multi-Scale Contrastive Learning	Sep 12, 2023	Anomaly DetectionContrastive Learning	CodeCode Available	1
Enhancing Representation in Radiography-Reports Foundation Model: A Granular Alignment Algorithm Using Masked Contrastive Learning	Sep 12, 2023	Contrastive LearningMedical Image Analysis	CodeCode Available	1
Grounded Language Acquisition From Object and Action Imagery	Sep 12, 2023	Action RecognitionContrastive Learning	—Unverified	0
Enhancing Hyperedge Prediction with Context-Aware Self-Supervised Learning	Sep 11, 2023	Contrastive LearningHyperedge Prediction	CodeCode Available	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