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 3531–3540 of 6661 papers

Title	Date	Tasks	Status
Federated Hyperdimensional Computing	Dec 26, 2023	Contrastive LearningFederated Learning	—Unverified
Federated Learning from Pre-Trained Models: A Contrastive Learning Approach	Sep 21, 2022	Contrastive LearningFederated Learning	—Unverified
Federated Prototype Graph Learning	Apr 13, 2025	Contrastive LearningGraph Learning	—Unverified
Towards Communication-Efficient and Privacy-Preserving Federated Representation Learning	Sep 29, 2021	Contrastive LearningFederated Learning	—Unverified
Federated Self-Supervised Contrastive Learning and Masked Autoencoder for Dermatological Disease Diagnosis	Aug 24, 2022	Contrastive LearningFederated Learning	—Unverified
FedIFL: A federated cross-domain diagnostic framework for motor-driven systems with inconsistent fault modes	May 12, 2025	Contrastive LearningDiagnostic	—Unverified
FedRGL: Robust Federated Graph Learning for Label Noise	Nov 28, 2024	Contrastive LearningGraph Learning	—Unverified
FedSA: A Unified Representation Learning via Semantic Anchors for Prototype-based Federated Learning	Jan 9, 2025	Classifier calibrationContrastive Learning	—Unverified
FedSeg: Class-Heterogeneous Federated Learning for Semantic Segmentation	Jan 1, 2023	Contrastive LearningFederated Learning	—Unverified
FedSiam-DA: Dual-aggregated Federated Learning via Siamese Network under Non-IID Data	Nov 17, 2022	Contrastive LearningFederated Learning	—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