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 1381–1390 of 6661 papers

Title	Date	Tasks	Status	Hype
Knowledge Graph Contrastive Learning for Recommendation	May 2, 2022	Contrastive LearningGeneral Knowledge	CodeCode Available	1
Direct Preference-based Policy Optimization without Reward Modeling	Jan 30, 2023	Contrastive LearningOffline RL	CodeCode Available	1
Learning Disentangled Representation by Exploiting Pretrained Generative Models: A Contrastive Learning View	Feb 21, 2021	Contrastive LearningDisentanglement	CodeCode Available	1
DetCo: Unsupervised Contrastive Learning for Object Detection	Feb 9, 2021	Contrastive Learningimage-classification	CodeCode Available	1
Label Contrastive Coding based Graph Neural Network for Graph Classification	Jan 14, 2021	ClassificationContrastive Learning	CodeCode Available	1
Label-Efficient Multi-Task Segmentation using Contrastive Learning	Sep 23, 2020	Contrastive LearningMulti-Task Learning	CodeCode Available	1
Detect Rumors in Microblog Posts for Low-Resource Domains via Adversarial Contrastive Learning	Apr 18, 2022	Contrastive Learning	CodeCode Available	1
From t-SNE to UMAP with contrastive learning	Jun 3, 2022	Contrastive LearningRepresentation Learning	CodeCode Available	1
Compressive Visual Representations	Sep 27, 2021	Contrastive LearningImage Classification	CodeCode Available	1
Dense Contrastive Learning for Self-Supervised Visual Pre-Training	Nov 18, 2020	Contrastive Learningimage-classification	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