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 4351–4360 of 6661 papers

Title	Date	Tasks	Status	Hype
QA Domain Adaptation using Hidden Space Augmentation and Self-Supervised Contrastive Adaptation	Oct 19, 2022	Contrastive LearningData Augmentation	CodeCode Available	0
Supervised Contrastive Learning with Tree-Structured Parzen Estimator Bayesian Optimization for Imbalanced Tabular Data	Oct 19, 2022	Bayesian OptimizationContrastive Learning	—Unverified	0
HAVANA: Hard negAtiVe sAmples aware self-supervised coNtrastive leArning for Airborne laser scanning point clouds semantic segmentation	Oct 19, 2022	Contrastive LearningSegmentation	—Unverified	0
DyTed: Disentangled Representation Learning for Discrete-time Dynamic Graph	Oct 19, 2022	Contrastive LearningDisentanglement	CodeCode Available	1
UniNL: Aligning Representation Learning with Scoring Function for OOD Detection via Unified Neighborhood Learning	Oct 19, 2022	Contrastive LearningOut of Distribution (OOD) Detection	CodeCode Available	0
Learning from the Dictionary: Heterogeneous Knowledge Guided Fine-tuning for Chinese Spell Checking	Oct 19, 2022	Chinese Spell CheckingContrastive Learning	CodeCode Available	1
Sentiment-Aware Word and Sentence Level Pre-training for Sentiment Analysis	Oct 18, 2022	Contrastive LearningLanguage Modeling	CodeCode Available	1
MedCLIP: Contrastive Learning from Unpaired Medical Images and Text	Oct 18, 2022	Contrastive LearningImage-text Retrieval	CodeCode Available	2
Universal hidden monotonic trend estimation with contrastive learning	Oct 18, 2022	Contrastive LearningTime Series	—Unverified	0
Unsupervised visualization of image datasets using contrastive learning	Oct 18, 2022	Contrastive LearningData Augmentation	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