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 1591–1600 of 6661 papers

Title	Date	Tasks	Status	Hype	Score
ASCON: Anatomy-aware Supervised Contrastive Learning Framework for Low-dose CT Denoising	Jul 23, 2023	AnatomyComputed Tomography (CT)	CodeCode Available	1	5
RODD: A Self-Supervised Approach for Robust Out-of-Distribution Detection	Apr 6, 2022	Contrastive LearningOut-of-Distribution Detection	CodeCode Available	1	5
Learning Audio-Visual Source Localization via False Negative Aware Contrastive Learning	Mar 20, 2023	Contrastive Learning	CodeCode Available	1	5
Hallucination Augmented Contrastive Learning for Multimodal Large Language Model	Dec 12, 2023	Contrastive LearningHallucination	CodeCode Available	1	5
Cross-Domain Graph Anomaly Detection via Anomaly-aware Contrastive Alignment	Dec 2, 2022	Anomaly DetectionContrastive Learning	CodeCode Available	1	5
Cross-Domain Sentiment Classification with Contrastive Learning and Mutual Information Maximization	Oct 30, 2020	Contrastive LearningGeneral Classification	CodeCode Available	1	5
Cross-Domain Sentiment Classification with In-Domain Contrastive Learning	Dec 5, 2020	ClassificationContrastive Learning	CodeCode Available	1	5
Hard Negative Mixing for Contrastive Learning	Oct 2, 2020	Contrastive LearningData Augmentation	CodeCode Available	1	5
Cross-level Contrastive Learning and Consistency Constraint for Semi-supervised Medical Image Segmentation	Feb 8, 2022	Contrastive LearningImage Segmentation	CodeCode Available	1	5
Learning Better Contrastive View from Radiologist's Gaze	May 15, 2023	Contrastive LearningData Augmentation	CodeCode Available	1	5

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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