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 2521–2530 of 6661 papers

Title	Date	Tasks	Status	Hype
Chasing Fairness in Graphs: A GNN Architecture Perspective	Dec 19, 2023	AttributeContrastive Learning	CodeCode Available	0
Time-Series Contrastive Learning against False Negatives and Class Imbalance	Dec 19, 2023	Contrastive LearningRepresentation Learning	—Unverified	0
FontDiffuser: One-Shot Font Generation via Denoising Diffusion with Multi-Scale Content Aggregation and Style Contrastive Learning	Dec 19, 2023	Contrastive LearningDenoising	CodeCode Available	2
SMC-NCA: Semantic-guided Multi-level Contrast for Semi-supervised Temporal Action Segmentation	Dec 19, 2023	Action SegmentationContrastive Learning	CodeCode Available	0
Generalized Category Discovery with Large Language Models in the Loop	Dec 18, 2023	Active LearningContrastive Learning	CodeCode Available	1
Cross-Age Contrastive Learning for Age-Invariant Face Recognition	Dec 18, 2023	Age-Invariant Face RecognitionContrastive Learning	—Unverified	0
Frequency Spectrum is More Effective for Multimodal Representation and Fusion: A Multimodal Spectrum Rumor Detector	Dec 18, 2023	Contrastive Learning	CodeCode Available	1
Hybrid Internal Model: Learning Agile Legged Locomotion with Simulated Robot Response	Dec 18, 2023	Contrastive Learning	CodeCode Available	2
ContraNovo: A Contrastive Learning Approach to Enhance De Novo Peptide Sequencing	Dec 18, 2023	Contrastive Learningde novo peptide sequencing	CodeCode Available	1
The Right Losses for the Right Gains: Improving the Semantic Consistency of Deep Text-to-Image Generation with Distribution-Sensitive Losses	Dec 18, 2023	Contrastive LearningImage Generation	—Unverified	0

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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