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 271–280 of 6661 papers

Title	Date	Tasks	Status	Hype	Score
CONTRASTE: Supervised Contrastive Pre-training With Aspect-based Prompts For Aspect Sentiment Triplet Extraction	Oct 24, 2023	Aspect Sentiment Triplet ExtractionAspect Term Extraction and Sentiment Classification	CodeCode Available	1	5
BadCLIP: Dual-Embedding Guided Backdoor Attack on Multimodal Contrastive Learning	Nov 20, 2023	Backdoor AttackContrastive Learning	CodeCode Available	1	5
A graph-transformer for whole slide image classification	May 19, 2022	ClassificationContrastive Learning	CodeCode Available	1	5
CoMAE: Single Model Hybrid Pre-training on Small-Scale RGB-D Datasets	Feb 13, 2023	Contrastive LearningRepresentation Learning	CodeCode Available	1	5
A Graph is Worth 1-bit Spikes: When Graph Contrastive Learning Meets Spiking Neural Networks	May 30, 2023	Contrastive LearningSelf-Supervised Learning	CodeCode Available	1	5
BadHash: Invisible Backdoor Attacks against Deep Hashing with Clean Label	Jul 1, 2022	Adversarial AttackBackdoor Attack	CodeCode Available	1	5
CoMatch: Semi-supervised Learning with Contrastive Graph Regularization	Nov 23, 2020	Contrastive LearningRepresentation Learning	CodeCode Available	1	5
CoLA: Weakly-Supervised Temporal Action Localization with Snippet Contrastive Learning	Mar 30, 2021	Action LocalizationCoLA	CodeCode Available	1	5
CoIn: Contrastive Instance Feature Mining for Outdoor 3D Object Detection with Very Limited Annotations	Jan 1, 2023	3D Object DetectionContrastive Learning	CodeCode Available	1	5
Collaborating Domain-shared and Target-specific Feature Clustering for Cross-domain 3D Action Recognition	Jul 20, 2022	3D Action RecognitionAction Recognition	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