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

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Showing 3511–3520 of 6661 papers

Title	Date	Tasks	Status
Feature Extraction Framework based on Contrastive Learning with Adaptive Positive and Negative Samples	Jan 11, 2022	Contrastive Learning	—Unverified
Feature Normalization Prevents Collapse of Non-contrastive Learning Dynamics	Sep 28, 2023	Computational EfficiencyContrastive Learning	—Unverified
Features-over-the-Air: Contrastive Learning Enabled Cooperative Edge Inference	Apr 17, 2023	Contrastive LearningImage Retrieval	—Unverified
FecTek: Enhancing Term Weight in Lexicon-Based Retrieval with Feature Context and Term-level Knowledge	Apr 18, 2024	Contrastive LearningRetrieval	—Unverified
FedCL: Federated Contrastive Learning for Privacy-Preserving Recommendation	Apr 21, 2022	Contrastive LearningPrivacy Preserving	—Unverified
Self-supervised On-device Federated Learning from Unlabeled Streams	Dec 2, 2022	Contrastive LearningFederated Learning	—Unverified
FedCPC: An Effective Federated Contrastive Learning Method for Privacy Preserving Early-Stage Alzheimer's Speech Detection	Nov 21, 2023	Contrastive LearningFederated Learning	—Unverified
FedCRL: Personalized Federated Learning with Contrastive Shared Representations for Label Heterogeneity in Non-IID Data	Apr 27, 2024	Contrastive LearningDistributed Computing	—Unverified
FedEPA: Enhancing Personalization and Modality Alignment in Multimodal Federated Learning	Apr 16, 2025	Contrastive LearningDiversity	—Unverified
Federated Contrastive Learning for Decentralized Unlabeled Medical Images	Sep 15, 2021	Contrastive LearningData Augmentation	—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