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 1241–1250 of 6661 papers

Title	Date	Tasks	Status	Hype	Score
Data Poisoning Attacks Against Multimodal Encoders	Sep 30, 2022	Contrastive LearningData Poisoning	CodeCode Available	1	5
Data Efficient Language-supervised Zero-shot Recognition with Optimal Transport Distillation	Dec 17, 2021	Contrastive LearningKnowledge Distillation	CodeCode Available	1	5
GroupContrast: Semantic-aware Self-supervised Representation Learning for 3D Understanding	Mar 14, 2024	Contrastive LearningRepresentation Learning	CodeCode Available	1	5
Joint Learning of Localized Representations from Medical Images and Reports	Dec 6, 2021	Contrastive Learningimage-classification	CodeCode Available	1	5
GraSS: Contrastive Learning with Gradient Guided Sampling Strategy for Remote Sensing Image Semantic Segmentation	Jun 28, 2023	Contrastive LearningSegmentation	CodeCode Available	1	5
Alleviating Exposure Bias via Contrastive Learning for Abstractive Text Summarization	Aug 26, 2021	Abstractive Text SummarizationContrastive Learning	CodeCode Available	1	5
Self-Supervised Pre-Training with Contrastive and Masked Autoencoder Methods for Dealing with Small Datasets in Deep Learning for Medical Imaging	Aug 12, 2023	Contrastive LearningDiagnostic	CodeCode Available	1	5
Debiased Contrastive Learning for Sequential Recommendation	Mar 21, 2023	Contrastive LearningData Augmentation	CodeCode Available	1	5
A Unified Generative Framework for Realistic Lidar Simulation in Autonomous Driving Systems	Dec 25, 2023	Autonomous DrivingContrastive Learning	CodeCode Available	1	5
GRENADE: Graph-Centric Language Model for Self-Supervised Representation Learning on Text-Attributed Graphs	Oct 23, 2023	Contrastive LearningGraph Neural Network	CodeCode Available	1	5

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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