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 1711–1720 of 6661 papers

Title	Date	Tasks	Status	Hype
Dyadic Interaction Modeling for Social Behavior Generation	Mar 14, 2024	Contrastive LearningDiversity	CodeCode Available	1
Dynamic Conceptional Contrastive Learning for Generalized Category Discovery	Mar 30, 2023	Contrastive LearningFine-Grained Visual Recognition	CodeCode Available	1
Empowering Collaborative Filtering with Principled Adversarial Contrastive Loss	Oct 28, 2023	Collaborative FilteringContrastive Learning	CodeCode Available	1
DC-Seg: Disentangled Contrastive Learning for Brain Tumor Segmentation with Missing Modalities	May 17, 2025	Brain Tumor SegmentationContrastive Learning	CodeCode Available	1
Dynamic Clustering and Cluster Contrastive Learning for Unsupervised Person Re-identification	Mar 13, 2023	ClusteringComputational Efficiency	CodeCode Available	1
Self-Supervised Contrastive Learning for Singing Voices	Apr 26, 2022	Contrastive LearningSinger Identification	CodeCode Available	1
Community-Invariant Graph Contrastive Learning	May 2, 2024	Contrastive Learning	CodeCode Available	1
Self-Supervised Pre-Training with Contrastive and Masked Autoencoder Methods for Dealing with Small Datasets in Deep Learning for Medical Imaging	Aug 12, 2023	Contrastive LearningDiagnostic	CodeCode Available	1
Self-supervised Graph Neural Networks without explicit negative sampling	Mar 27, 2021	Contrastive LearningData Augmentation	CodeCode Available	1
Energy-based learning algorithms for analog computing: a comparative study	Dec 22, 2023	Contrastive 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