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 1951–1960 of 6661 papers

Title	Date	Tasks	Status	Score
Multi-level Contrastive Learning for Script-based Character Understanding	Oct 20, 2023	Contrastive Learning	CodeCode Available	5
CMIP-CIL: A Cross-Modal Benchmark for Image-Point Class Incremental Learning	Apr 11, 2025	class-incremental learningClass Incremental Learning	CodeCode Available	5
Disentangled Modeling of Preferences and Social Influence for Group Recommendation	Jan 20, 2025	Contrastive LearningSelf-Supervised Learning	CodeCode Available	5
MolPLA: A Molecular Pretraining Framework for Learning Cores, R-Groups and their Linker Joints	Jan 30, 2024	Contrastive LearningMolecular Property Prediction	CodeCode Available	5
Multi-level Matching Network for Multimodal Entity Linking	Dec 11, 2024	Contrastive LearningEntity Linking	CodeCode Available	5
Multi-Level Sequence Denoising with Cross-Signal Contrastive Learning for Sequential Recommendation	Apr 22, 2024	Contrastive LearningDenoising	CodeCode Available	5
Molecular Graph Contrastive Learning with Line Graph	Jan 15, 2025	AttributeContrastive Learning	CodeCode Available	5
MoMA: Momentum Contrastive Learning with Multi-head Attention-based Knowledge Distillation for Histopathology Image Analysis	Aug 31, 2023	Contrastive LearningKnowledge Distillation	CodeCode Available	5
Disentangled Contrastive Learning for Social Recommendation	Aug 18, 2022	Contrastive LearningRepresentation Learning	CodeCode Available	5
Disentangled Contrastive Learning for Learning Robust Textual Representations	Apr 11, 2021	Contrastive Learning	CodeCode Available	5

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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