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 1761–1770 of 6661 papers

Title	Date	Tasks	Status	Hype
Cross-Domain Sentiment Classification with In-Domain Contrastive Learning	Dec 5, 2020	ClassificationContrastive Learning	CodeCode Available	1
SAM: Self-supervised Learning of Pixel-wise Anatomical Embeddings in Radiological Images	Dec 4, 2020	AnatomyComputed Tomography (CT)	CodeCode Available	1
Pre-Trained Image Processing Transformer	Dec 1, 2020	Color Image DenoisingContrastive Learning	CodeCode Available	1
Multi-level Knowledge Distillation via Knowledge Alignment and Correlation	Dec 1, 2020	Contrastive LearningKnowledge Distillation	CodeCode Available	1
Soft Contrastive Learning for Visual Localization	Dec 1, 2020	Contrastive LearningImage Retrieval	CodeCode Available	1
Time Series Change Point Detection with Self-Supervised Contrastive Predictive Coding	Nov 28, 2020	Anomaly DetectionChange Point Detection	CodeCode Available	1
Self supervised contrastive learning for digital histopathology	Nov 27, 2020	Contrastive LearningMedical Image Analysis	CodeCode Available	1
USCL: Pretraining Deep Ultrasound Image Diagnosis Model through Video Contrastive Representation Learning	Nov 25, 2020	Contrastive LearningMedical Image Analysis	CodeCode Available	1
CoMatch: Semi-supervised Learning with Contrastive Graph Regularization	Nov 23, 2020	Contrastive LearningRepresentation Learning	CodeCode Available	1
Boosting Contrastive Self-Supervised Learning with False Negative Cancellation	Nov 23, 2020	Contrastive LearningRepresentation 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