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 2731–2740 of 6661 papers

Title	Date	Tasks	Status	Hype
Rethinking Samples Selection for Contrastive Learning: Mining of Potential Samples	Nov 1, 2023	Contrastive LearningData Augmentation	—Unverified	0
BasisFormer: Attention-based Time Series Forecasting with Learnable and Interpretable Basis	Oct 31, 2023	Contrastive LearningSelf-Supervised Learning	CodeCode Available	1
Medi-CAT: Contrastive Adversarial Training for Medical Image Classification	Oct 31, 2023	Contrastive Learningimage-classification	—Unverified	0
SimMMDG: A Simple and Effective Framework for Multi-modal Domain Generalization	Oct 30, 2023	Contrastive LearningDiversity	CodeCode Available	1
FOCAL: Contrastive Learning for Multimodal Time-Series Sensing Signals in Factorized Orthogonal Latent Space	Oct 30, 2023	Contrastive LearningTime Series	CodeCode Available	1
Uncertainty-guided Boundary Learning for Imbalanced Social Event Detection	Oct 30, 2023	Contrastive LearningEvent Detection	CodeCode Available	0
Improving Medical Visual Representations via Radiology Report Generation	Oct 30, 2023	Contrastive LearningDecoder	—Unverified	0
FLIP: Fine-grained Alignment between ID-based Models and Pretrained Language Models for CTR Prediction	Oct 30, 2023	Click-Through Rate PredictionContrastive Learning	CodeCode Available	1
Adversarial Bootstrapped Question Representation Learning for Knowledge Tracing	Oct 29, 2023	Contrastive LearningKnowledge Tracing	CodeCode Available	0
Adversarial Examples Are Not Real Features	Oct 29, 2023	Contrastive LearningSelf-Supervised 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