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 3191–3200 of 6661 papers

Title	Date	Tasks	Status
Function Contrastive Learning of Transferable Meta-Representations	Oct 14, 2020	Contrastive LearningDecoder	—Unverified
Function Contrastive Learning of Transferable Representations	Sep 28, 2020	Contrastive LearningFew-Shot Learning	—Unverified
Functional Graph Contrastive Learning of Hyperscanning EEG Reveals Emotional Contagion Evoked by Stereotype-Based Stressors	Aug 22, 2023	Contrastive LearningEEG	—Unverified
Contrastive Learning for Time Series on Dynamic Graphs	Sep 21, 2022	Activity RecognitionAnomaly Detection	—Unverified
Learning a Domain-Agnostic Visual Representation for Autonomous Driving via Contrastive Loss	Mar 10, 2021	Autonomous DrivingContrastive Learning	—Unverified
Incorporating Attributes and Multi-Scale Structures for Heterogeneous Graph Contrastive Learning	Mar 18, 2025	AttributeContrastive Learning	—Unverified
Incorporating Dense Knowledge Alignment into Unified Multimodal Representation Models	Jan 1, 2025	Contrastive LearningCross-Modal Retrieval	—Unverified
Cross-domain Transfer of defect features in technical domains based on partial target data	Nov 24, 2022	ClassificationContrastive Learning	—Unverified
FSSUAVL: A Discriminative Framework using Vision Models for Federated Self-Supervised Audio and Image Understanding	Apr 13, 2025	Contrastive LearningFederated Learning	—Unverified
A Novel Transformer-Based Self-Supervised Learning Method to Enhance Photoplethysmogram Signal Artifact Detection	Jan 2, 2024	Artifact DetectionContrastive 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