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 1771–1780 of 6661 papers

Title	Date	Tasks	Status	Hype
Boosting Contrastive Self-Supervised Learning with False Negative Cancellation	Nov 23, 2020	Contrastive LearningRepresentation Learning	CodeCode Available	1
Propagate Yourself: Exploring Pixel-Level Consistency for Unsupervised Visual Representation Learning	Nov 19, 2020	Contrastive Learningobject-detection	CodeCode Available	1
Geography-Aware Self-Supervised Learning	Nov 19, 2020	Contrastive Learningimage-classification	CodeCode Available	1
Dense Contrastive Learning for Self-Supervised Visual Pre-Training	Nov 18, 2020	Contrastive Learningimage-classification	CodeCode Available	1
Contrastive Registration for Unsupervised Medical Image Segmentation	Nov 17, 2020	Contrastive LearningImage Segmentation	CodeCode Available	1
AdCo: Adversarial Contrast for Efficient Learning of Unsupervised Representations from Self-Trained Negative Adversaries	Nov 17, 2020	Contrastive LearningLinear evaluation	CodeCode Available	1
Dual-stream Multiple Instance Learning Network for Whole Slide Image Classification with Self-supervised Contrastive Learning	Nov 17, 2020	ClassificationContrastive Learning	CodeCode Available	1
Unsupervised Contrastive Learning of Sound Event Representations	Nov 15, 2020	Contrastive LearningLinear evaluation	CodeCode Available	1
CODER: Knowledge infused cross-lingual medical term embedding for term normalization	Nov 5, 2020	Contrastive LearningKnowledge Graphs	CodeCode Available	1
Learning and Evaluating Representations for Deep One-class Classification	Nov 4, 2020	Anomaly DetectionClassification	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