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 551–560 of 6661 papers

Title	Date	Tasks	Status	Hype
Refine Knowledge of Large Language Models via Adaptive Contrastive Learning	Feb 11, 2025	Contrastive LearningHallucination	—Unverified	0
Supervised contrastive learning for cell stage classification of animal embryos	Feb 11, 2025	Contrastive Learning	—Unverified	0
Learning Clustering-based Prototypes for Compositional Zero-shot Learning	Feb 10, 2025	AttributeClustering	CodeCode Available	1
Multimodal Task Representation Memory Bank vs. Catastrophic Forgetting in Anomaly Detection	Feb 10, 2025	Anomaly DetectionContrastive Learning	—Unverified	0
Unleashing the Potential of Pre-Trained Diffusion Models for Generalizable Person Re-Identification	Feb 10, 2025	Contrastive LearningGeneralizable Person Re-identification	CodeCode Available	0
Structure-preserving contrastive learning for spatial time series	Feb 10, 2025	Contrastive LearningRepresentation Learning	CodeCode Available	0
RAMer: Reconstruction-based Adversarial Model for Multi-party Multi-modal Multi-label Emotion Recognition	Feb 9, 2025	Contrastive LearningEmotion Recognition	CodeCode Available	0
Group Reasoning Emission Estimation Networks	Feb 8, 2025	ClassificationContrastive Learning	—Unverified	0
Learning Street View Representations with Spatiotemporal Contrast	Feb 7, 2025	Contrastive LearningRepresentation Learning	CodeCode Available	0
Self-Supervised Learning for Pre-training Capsule Networks: Overcoming Medical Imaging Dataset Challenges	Feb 7, 2025	Contrastive LearningDiagnostic	—Unverified	0

Show:10 25 50

← PrevPage 56 of 667Next →

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