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 311–320 of 6661 papers

Title	Date	Tasks	Status	Hype	Score
Bridging Spectral-wise and Multi-spectral Depth Estimation via Geometry-guided Contrastive Learning	Mar 2, 2025	Autonomous VehiclesContrastive Learning	CodeCode Available	1	5
BasisFormer: Attention-based Time Series Forecasting with Learnable and Interpretable Basis	Oct 31, 2023	Contrastive LearningSelf-Supervised Learning	CodeCode Available	1	5
Continuous Learning for Android Malware Detection	Feb 8, 2023	Active LearningAndroid Malware Detection	CodeCode Available	1	5
ContraNorm: A Contrastive Learning Perspective on Oversmoothing and Beyond	Mar 12, 2023	Contrastive Learning	CodeCode Available	1	5
Adversarial Training of Self-supervised Monocular Depth Estimation against Physical-World Attacks	Jan 31, 2023	Adversarial RobustnessAutonomous Driving	CodeCode Available	1	5
Temporal Context Aggregation for Video Retrieval with Contrastive Learning	Aug 4, 2020	Contrastive LearningRepresentation Learning	CodeCode Available	1	5
3D Interaction Geometric Pre-training for Molecular Relational Learning	Dec 4, 2024	Contrastive LearningDrug Discovery	CodeCode Available	1	5
Adversarial Self-Supervised Contrastive Learning	Jun 13, 2020	Adversarial AttackContrastive Learning	CodeCode Available	1	5
Context-self contrastive pretraining for crop type semantic segmentation	Apr 9, 2021	Contrastive LearningSegmentation	CodeCode Available	1	5
Balanced Contrastive Learning for Long-Tailed Visual Recognition	Jul 19, 2022	Contrastive LearningImage Classification	CodeCode Available	1	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