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 641–650 of 6661 papers

Title	Date	Tasks	Status	Hype
Contrastive Representation Learning for Exemplar-Guided Paraphrase Generation	Sep 3, 2021	Contrastive LearningDecoder	CodeCode Available	1
Contrastive Representation Learning for Dynamic Link Prediction in Temporal Networks	Aug 22, 2024	Contrastive LearningDynamic Link Prediction	CodeCode Available	1
Contrastive Learning from Spatio-Temporal Mixed Skeleton Sequences for Self-Supervised Skeleton-Based Action Recognition	Jul 7, 2022	Action RecognitionContrastive Learning	CodeCode Available	1
Boosting Contrastive Self-Supervised Learning with False Negative Cancellation	Nov 23, 2020	Contrastive LearningRepresentation Learning	CodeCode Available	1
Contrastive Semi-supervised Learning for Domain Adaptive Segmentation Across Similar Anatomical Structures	Aug 18, 2022	AnatomyContrastive Learning	CodeCode Available	1
Boosting Few-Shot Classification with View-Learnable Contrastive Learning	Jul 20, 2021	ClassificationContrastive Learning	CodeCode Available	1
Contrastive Fine-grained Class Clustering via Generative Adversarial Networks	Dec 30, 2021	ClusteringContrastive Learning	CodeCode Available	1
Contrastive Trajectory Similarity Learning with Dual-Feature Attention	Oct 11, 2022	Contrastive LearningTrajectory Modeling	CodeCode Available	1
AD-L-JEPA: Self-Supervised Spatial World Models with Joint Embedding Predictive Architecture for Autonomous Driving with LiDAR Data	Jan 9, 2025	3D Object DetectionAutonomous Driving	CodeCode Available	1
Benchmarking Omni-Vision Representation through the Lens of Visual Realms	Jul 14, 2022	BenchmarkingContrastive Learning	CodeCode Available	1

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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