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 2461–2470 of 6661 papers

Title	Date	Tasks	Status
HAVANA: Hard negAtiVe sAmples aware self-supervised coNtrastive leArning for Airborne laser scanning point clouds semantic segmentation	Oct 19, 2022	Contrastive LearningSegmentation	—Unverified
Cross-modal Contrastive Learning for Speech Translation	Dec 17, 2021	Contrastive LearningRetrieval	—Unverified
3D-Consistent Human Avatars with Sparse Inputs via Gaussian Splatting and Contrastive Learning	Aug 19, 2024	3D geometry3DGS	—Unverified
Mastering Long-Tail Complexity on Graphs: Characterization, Learning, and Generalization	May 17, 2023	ClassificationContrastive Learning	—Unverified
Hard Sample Mining Enabled Supervised Contrastive Feature Learning for Wind Turbine Pitch System Fault Diagnosis	Jun 26, 2023	Contrastive LearningFault Diagnosis	—Unverified
Knowledge-Augmented Contrastive Learning for Abnormality Classification and Localization in Chest X-rays with Radiomics using a Feedback Loop	Apr 11, 2021	Contrastive Learning	—Unverified
Chaos is a Ladder: A New Understanding of Contrastive Learning	Sep 29, 2021	Contrastive LearningSelf-Supervised Learning	—Unverified
Cross-Modal Attention Consistency for Video-Audio Unsupervised Learning	Jun 13, 2021	Contrastive LearningRepresentation Learning	—Unverified
Affinity-Graph-Guided Contractive Learning for Pretext-Free Medical Image Segmentation with Minimal Annotation	Oct 14, 2024	Contrastive LearningImage Segmentation	—Unverified
Harmony: A Generic Unsupervised Approach for Disentangling Semantic Content From Parameterized Transformations	Jan 1, 2022	Contrastive LearningDisentanglement	—Unverified

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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