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 1681–1690 of 6661 papers

Title	Date	Tasks	Status	Hype
Prototypical Cross-domain Self-supervised Learning for Few-shot Unsupervised Domain Adaptation	Mar 31, 2021	Contrastive LearningDomain Adaptation	CodeCode Available	1
Rethinking Self-supervised Correspondence Learning: A Video Frame-level Similarity Perspective	Mar 31, 2021	Contrastive LearningObject	CodeCode Available	1
CoLA: Weakly-Supervised Temporal Action Localization with Snippet Contrastive Learning	Mar 30, 2021	Action LocalizationCoLA	CodeCode Available	1
Model-Contrastive Federated Learning	Mar 30, 2021	Contrastive LearningFederated Learning	CodeCode Available	1
Progressive Domain Expansion Network for Single Domain Generalization	Mar 30, 2021	Contrastive LearningDomain Generalization	CodeCode Available	1
ICE: Inter-instance Contrastive Encoding for Unsupervised Person Re-identification	Mar 30, 2021	Contrastive LearningPerson Re-Identification	CodeCode Available	1
Denoise and Contrast for Category Agnostic Shape Completion	Mar 30, 2021	Contrastive LearningDenoising	CodeCode Available	1
Self-supervised Graph Neural Networks without explicit negative sampling	Mar 27, 2021	Contrastive LearningData Augmentation	CodeCode Available	1
Rethinking Self-Supervised Learning: Small is Beautiful	Mar 25, 2021	Contrastive LearningSelf-Supervised Learning	CodeCode Available	1
Supporting Clustering with Contrastive Learning	Mar 24, 2021	ClusteringContrastive 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