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 4721–4730 of 6661 papers

Title	Date	Tasks	Status	Hype
ConCL: Concept Contrastive Learning for Dense Prediction Pre-training in Pathology Images	Jul 14, 2022	Contrastive LearningSaliency Detection	CodeCode Available	1
Deep Image Clustering with Contrastive Learning and Multi-scale Graph Convolutional Networks	Jul 14, 2022	ClusteringContrastive Learning	CodeCode Available	1
An Asymmetric Contrastive Loss for Handling Imbalanced Datasets	Jul 14, 2022	Contrastive LearningRepresentation Learning	CodeCode Available	0
Contrastive Adapters for Foundation Model Group Robustness	Jul 14, 2022	Contrastive Learningmodel	—Unverified	0
Prototypical Contrast Adaptation for Domain Adaptive Semantic Segmentation	Jul 14, 2022	Contrastive LearningDomain Adaptation	CodeCode Available	0
Rich Feature Distillation with Feature Affinity Module for Efficient Image Dehazing	Jul 13, 2022	Contrastive Learningimage-classification	—Unverified	0
Multiview Contrastive Learning for Completely Blind Video Quality Assessment of User Generated Content	Jul 13, 2022	Contrastive LearningOptical Flow Estimation	CodeCode Available	0
Unsupervised Visual Representation Learning by Synchronous Momentum Grouping	Jul 13, 2022	ClusteringContrastive Learning	—Unverified	0
Self-supervised Group Meiosis Contrastive Learning for EEG-Based Emotion Recognition	Jul 12, 2022	Contrastive LearningData Augmentation	CodeCode Available	1
Dual Contrastive Learning for Spatio-temporal Representation	Jul 12, 2022	Contrastive LearningRepresentation Learning	—Unverified	0

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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