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 3611–3620 of 6661 papers

Title	Date	Tasks	Status
From Patches to Objects: Exploiting Spatial Reasoning for Better Visual Representations	May 21, 2023	Contrastive LearningLinear evaluation	—Unverified
From Pixels to Prose: Advancing Multi-Modal Language Models for Remote Sensing	Nov 5, 2024	Change DetectionContrastive Learning	—Unverified
From Pretext to Purpose: Batch-Adaptive Self-Supervised Learning	Nov 16, 2023	Contrastive LearningDimensionality Reduction	—Unverified
From Real Artifacts to Virtual Reference: A Robust Framework for Translating Endoscopic Images	Oct 15, 2024	Contrastive LearningDenoising	—Unverified
From Unsupervised to Few-shot Graph Anomaly Detection: A Multi-scale Contrastive Learning Approach	Feb 11, 2022	Anomaly DetectionContrastive Learning	—Unverified
FSCIL-SEI: Few-Shot Class-Incremental Learning Approach for Specific Emitter Identification	Mar 3, 2025	class-incremental learningClass Incremental Learning	—Unverified
FSSUAVL: A Discriminative Framework using Vision Models for Federated Self-Supervised Audio and Image Understanding	Apr 13, 2025	Contrastive LearningFederated Learning	—Unverified
Functional Graph Contrastive Learning of Hyperscanning EEG Reveals Emotional Contagion Evoked by Stereotype-Based Stressors	Aug 22, 2023	Contrastive LearningEEG	—Unverified
Function Contrastive Learning of Transferable Representations	Sep 28, 2020	Contrastive LearningFew-Shot Learning	—Unverified
Function Contrastive Learning of Transferable Meta-Representations	Oct 14, 2020	Contrastive LearningDecoder	—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