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 4751–4760 of 6661 papers

Title	Date	Tasks	Status	Hype
MMGL: Multi-Scale Multi-View Global-Local Contrastive learning for Semi-supervised Cardiac Image Segmentation	Jul 5, 2022	Contrastive LearningImage Segmentation	CodeCode Available	0
Open-Vocabulary 3D Detection via Image-level Class and Debiased Cross-modal Contrastive Learning	Jul 5, 2022	Cloud DetectionContrastive Learning	—Unverified	0
Features Based Adaptive Augmentation for Graph Contrastive Learning	Jul 5, 2022	Contrastive LearningGraph Representation Learning	CodeCode Available	0
Block-SCL: Blocking Matters for Supervised Contrastive Learning in Product Matching	Jul 5, 2022	BlockingContrastive Learning	—Unverified	0
Multi-Modal Multi-Correlation Learning for Audio-Visual Speech Separation	Jul 4, 2022	Contrastive LearningSpeech Separation	—Unverified	0
Partial and Asymmetric Contrastive Learning for Out-of-Distribution Detection in Long-Tailed Recognition	Jul 4, 2022	Anomaly DetectionContrastive Learning	CodeCode Available	1
Game State Learning via Game Scene Augmentation	Jul 4, 2022	Contrastive LearningImage Augmentation	—Unverified	0
Positive-Negative Equal Contrastive Loss for Semantic Segmentation	Jul 4, 2022	Contrastive LearningSemantic Segmentation	—Unverified	0
Embedding contrastive unsupervised features to cluster in- and out-of-distribution noise in corrupted image datasets	Jul 4, 2022	ClusteringContrastive Learning	CodeCode Available	1
Multi-granularity Item-based Contrastive Recommendation	Jul 4, 2022	Contrastive LearningRecommendation Systems	—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