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 1141–1150 of 6661 papers

Title	Date	Tasks	Status	Hype
BankNote-Net: Open dataset for assistive universal currency recognition	Apr 7, 2022	Contrastive LearningFew-Shot Learning	CodeCode Available	1
Disentangled Causal Embedding With Contrastive Learning For Recommender System	Feb 7, 2023	Contrastive LearningRecommendation Systems	CodeCode Available	1
ContraNorm: A Contrastive Learning Perspective on Oversmoothing and Beyond	Mar 12, 2023	Contrastive Learning	CodeCode Available	1
ContraNovo: A Contrastive Learning Approach to Enhance De Novo Peptide Sequencing	Dec 18, 2023	Contrastive Learningde novo peptide sequencing	CodeCode Available	1
BasisFormer: Attention-based Time Series Forecasting with Learnable and Interpretable Basis	Oct 31, 2023	Contrastive LearningSelf-Supervised Learning	CodeCode Available	1
Dissolving Is Amplifying: Towards Fine-Grained Anomaly Detection	Feb 28, 2023	Anomaly DetectionContrastive Learning	CodeCode Available	1
Cross-Domain Sentiment Classification with Contrastive Learning and Mutual Information Maximization	Oct 30, 2020	Contrastive LearningGeneral Classification	CodeCode Available	1
Distilling Visual Priors from Self-Supervised Learning	Aug 1, 2020	ClassificationContrastive Learning	CodeCode Available	1
Contrast and Classify: Training Robust VQA Models	Oct 13, 2020	Contrastive LearningData Augmentation	CodeCode Available	1
CRIS: CLIP-Driven Referring Image Segmentation	Nov 30, 2021	Contrastive LearningDecoder	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