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 731–740 of 6661 papers

Title	Date	Tasks	Status	Hype
Source-Free Domain Adaptation for Medical Image Segmentation via Prototype-Anchored Feature Alignment and Contrastive Learning	Jul 19, 2023	Contrastive LearningDomain Adaptation	CodeCode Available	1
Semantic-Aware Dual Contrastive Learning for Multi-label Image Classification	Jul 19, 2023	Contrastive Learningimage-classification	CodeCode Available	1
Density-invariant Features for Distant Point Cloud Registration	Jul 19, 2023	Autonomous VehiclesContrastive Learning	CodeCode Available	1
Multi-Grained Multimodal Interaction Network for Entity Linking	Jul 19, 2023	Contrastive LearningDescriptive	CodeCode Available	1
Neural Architecture Retrieval	Jul 16, 2023	Contrastive LearningGraph Representation Learning	CodeCode Available	1
Contrastive Learning for Conversion Rate Prediction	Jul 12, 2023	Contrastive LearningPrediction	CodeCode Available	1
Mini-Batch Optimization of Contrastive Loss	Jul 12, 2023	Contrastive LearningSelf-Supervised Learning	CodeCode Available	1
Generative Contrastive Graph Learning for Recommendation	Jul 11, 2023	Collaborative FilteringContrastive Learning	CodeCode Available	1
mCLIP: Multilingual CLIP via Cross-lingual Transfer	Jul 10, 2023	Contrastive LearningCross-Lingual Transfer	CodeCode Available	1
Weakly-supervised positional contrastive learning: application to cirrhosis classification	Jul 10, 2023	ClassificationContrastive Learning	CodeCode Available	1

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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