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 1741–1750 of 6661 papers

Title	Date	Tasks	Status	Hype
Cross-Modal Contrastive Learning of Representations for Navigation using Lightweight, Low-Cost Millimeter Wave Radar for Adverse Environmental Conditions	Jan 10, 2021	Autonomous NavigationContrastive Learning	CodeCode Available	1
GAN-Control: Explicitly Controllable GANs	Jan 7, 2021	Contrastive LearningDisentanglement	CodeCode Available	1
Co2L: Contrastive Continual Learning	Jan 1, 2021	Continual LearningContrastive Learning	CodeCode Available	1
Unsupervised Point Cloud Object Co-Segmentation by Co-Contrastive Learning and Mutual Attention Sampling	Jan 1, 2021	Contrastive LearningObject	CodeCode Available	1
Learning From Noisy Data With Robust Representation Learning	Jan 1, 2021	Contrastive LearningRepresentation Learning	CodeCode Available	1
Region-Aware Contrastive Learning for Semantic Segmentation	Jan 1, 2021	Contrastive LearningSemantic Segmentation	CodeCode Available	1
Single Image 3D Shape Retrieval via Cross-Modal Instance and Category Contrastive Learning	Jan 1, 2021	3D Shape RetrievalContrastive Learning	CodeCode Available	1
ERICA: Improving Entity and Relation Understanding for Pre-trained Language Models via Contrastive Learning	Dec 30, 2020	Contrastive LearningEntity Typing	CodeCode Available	1
Spatial Contrastive Learning for Few-Shot Classification	Dec 26, 2020	ClassificationContrastive Learning	CodeCode Available	1
Self-Supervised Representation Learning for Astronomical Images	Dec 24, 2020	AstronomyContrastive 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