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

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Showing 411–420 of 6661 papers

Title	Date	Tasks	Status	Hype	Score
Chameleon: Adapting to Peer Images for Planting Durable Backdoors in Federated Learning	Apr 25, 2023	Contrastive LearningFederated Learning	CodeCode Available	1	5
Chaos is a Ladder: A New Theoretical Understanding of Contrastive Learning via Augmentation Overlap	Mar 25, 2022	ARCContrastive Learning	CodeCode Available	1	5
A Simple and Effective Self-Supervised Contrastive Learning Framework for Aspect Detection	Sep 18, 2020	Contrastive LearningTopic Models	CodeCode Available	1	5
Change-Aware Sampling and Contrastive Learning for Satellite Images	Jan 1, 2023	Change DetectionContrastive Learning	CodeCode Available	1	5
Bridging Mini-Batch and Asymptotic Analysis in Contrastive Learning: From InfoNCE to Kernel-Based Losses	May 28, 2024	Contrastive LearningRepresentation Learning	CodeCode Available	1	5
A Simple Contrastive Learning Objective for Alleviating Neural Text Degeneration	May 5, 2022	Contrastive LearningDialogue Generation	CodeCode Available	1	5
A simple, efficient and scalable contrastive masked autoencoder for learning visual representations	Oct 30, 2022	Contrastive LearningSelf-Supervised Learning	CodeCode Available	1	5
Anomaly Detection in IR Images of PV Modules using Supervised Contrastive Learning	Dec 6, 2021	Anomaly DetectionContrastive Learning	CodeCode Available	1	5
CL4CTR: A Contrastive Learning Framework for CTR Prediction	Dec 1, 2022	Click-Through Rate PredictionContrastive Learning	CodeCode Available	1	5
DiffAug: Enhance Unsupervised Contrastive Learning with Domain-Knowledge-Free Diffusion-based Data Augmentation	Sep 10, 2023	Contrastive LearningData Augmentation	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