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 3741–3750 of 6661 papers

Title	Date	Tasks	Status	Hype
On the Effects of Self-supervision and Contrastive Alignment in Deep Multi-view Clustering	Mar 17, 2023	ClusteringContrastive Learning	CodeCode Available	1
MassNet: A Deep Learning Approach for Body Weight Extraction from A Single Pressure Image	Mar 17, 2023	Contrastive LearningPrivacy Preserving	CodeCode Available	0
Investigating the Role of Attribute Context in Vision-Language Models for Object Recognition and Detection	Mar 17, 2023	AttributeContrastive Learning	—Unverified	0
SmartBERT: A Promotion of Dynamic Early Exiting Mechanism for Accelerating BERT Inference	Mar 16, 2023	Contrastive LearningLanguage Modelling	—Unverified	0
Steering Prototypes with Prompt-tuning for Rehearsal-free Continual Learning	Mar 16, 2023	class-incremental learningClass Incremental Learning	CodeCode Available	1
All4One: Symbiotic Neighbour Contrastive Learning via Self-Attention and Redundancy Reduction	Mar 16, 2023	Computational EfficiencyContrastive Learning	CodeCode Available	0
Unsupervised Facial Expression Representation Learning with Contrastive Local Warping	Mar 16, 2023	Contrastive LearningFacial Expression Recognition	CodeCode Available	0
Identifiability Results for Multimodal Contrastive Learning	Mar 16, 2023	Contrastive LearningRepresentation Learning	CodeCode Available	1
Spherical Space Feature Decomposition for Guided Depth Map Super-Resolution	Mar 15, 2023	Contrastive LearningDepth Map Super-Resolution	CodeCode Available	0
Graph-less Collaborative Filtering	Mar 15, 2023	Collaborative FilteringContrastive 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