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 1651–1660 of 6661 papers

Title	Date	Tasks	Status	Hype
Entailment as Few-Shot Learner	Apr 29, 2021	Contrastive LearningData Augmentation	CodeCode Available	1
Improving BERT Model Using Contrastive Learning for Biomedical Relation Extraction	Apr 28, 2021	Contrastive LearningData Augmentation	CodeCode Available	1
A Note on Connecting Barlow Twins with Negative-Sample-Free Contrastive Learning	Apr 28, 2021	Contrastive LearningSelf-Supervised Learning	CodeCode Available	1
Semi-Supervised Semantic Segmentation with Pixel-Level Contrastive Learning from a Class-wise Memory Bank	Apr 27, 2021	Contrastive LearningDomain Adaptation	CodeCode Available	1
Self-supervised Spatial Reasoning on Multi-View Line Drawings	Apr 27, 2021	Binary ClassificationContrastive Learning	CodeCode Available	1
Multimodal Clustering Networks for Self-supervised Learning from Unlabeled Videos	Apr 26, 2021	Action LocalizationClustering	CodeCode Available	1
Mutual Contrastive Learning for Visual Representation Learning	Apr 26, 2021	Contrastive LearningFew-Shot Learning	CodeCode Available	1
Pri3D: Can 3D Priors Help 2D Representation Learning?	Apr 22, 2021	Contrastive LearningInstance Segmentation	CodeCode Available	1
Distilling Audio-Visual Knowledge by Compositional Contrastive Learning	Apr 22, 2021	Audio Taggingaudio-visual learning	CodeCode Available	1
SelfReg: Self-supervised Contrastive Regularization for Domain Generalization	Apr 20, 2021	Contrastive LearningDomain Generalization	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