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 1861–1870 of 6661 papers

Title	Date	Tasks	Status	Hype
Self-paced Contrastive Learning with Hybrid Memory for Domain Adaptive Object Re-ID	Jun 4, 2020	ClusteringContrastive Learning	CodeCode Available	1
CLOCS: Contrastive Learning of Cardiac Signals Across Space, Time, and Patients	May 27, 2020	Contrastive LearningLinear evaluation	CodeCode Available	1
Network Comparison with Interpretable Contrastive Network Representation Learning	May 25, 2020	Contrastive LearningRepresentation Learning	CodeCode Available	1
Understanding Contrastive Representation Learning through Alignment and Uniformity on the Hypersphere	May 20, 2020	Contrastive LearningRepresentation Learning	CodeCode Available	1
ViTAA: Visual-Textual Attributes Alignment in Person Search by Natural Language	May 15, 2020	AttributeContrastive Learning	CodeCode Available	1
Prototypical Contrastive Learning of Unsupervised Representations	May 11, 2020	ClusteringContrastive Learning	CodeCode Available	1
Words aren't enough, their order matters: On the Robustness of Grounding Visual Referring Expressions	May 4, 2020	Contrastive LearningMulti-Task Learning	CodeCode Available	1
Image Augmentation Is All You Need: Regularizing Deep Reinforcement Learning from Pixels	Apr 28, 2020	AllAtari Games 100k	CodeCode Available	1
Audio-Visual Instance Discrimination with Cross-Modal Agreement	Apr 27, 2020	Action RecognitionAudio Classification	CodeCode Available	1
Disentangled and Controllable Face Image Generation via 3D Imitative-Contrastive Learning	Apr 24, 2020	Contrastive LearningDisentanglement	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