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 1041–1050 of 6661 papers

Title	Date	Tasks	Status	Hype	Score
BadCLIP: Dual-Embedding Guided Backdoor Attack on Multimodal Contrastive Learning	Nov 20, 2023	Backdoor AttackContrastive Learning	CodeCode Available	1	5
FedHCDR: Federated Cross-Domain Recommendation with Hypergraph Signal Decoupling	Mar 5, 2024	Contrastive LearningData Augmentation	CodeCode Available	1	5
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
Contrast Everything: A Hierarchical Contrastive Framework for Medical Time-Series	Oct 21, 2023	Contrastive LearningEEG	CodeCode Available	1	5
Contrasting with Symile: Simple Model-Agnostic Representation Learning for Unlimited Modalities	Nov 1, 2024	Contrastive LearningRepresentation Learning	CodeCode Available	1	5
ContrastCAD: Contrastive Learning-based Representation Learning for Computer-Aided Design Models	Apr 2, 2024	Contrastive LearningData Augmentation	CodeCode Available	1	5
FLIP: Fine-grained Alignment between ID-based Models and Pretrained Language Models for CTR Prediction	Oct 30, 2023	Click-Through Rate PredictionContrastive Learning	CodeCode Available	1	5
FedACK: Federated Adversarial Contrastive Knowledge Distillation for Cross-Lingual and Cross-Model Social Bot Detection	Mar 10, 2023	Contrastive LearningKnowledge Distillation	CodeCode Available	1	5
FedX: Unsupervised Federated Learning with Cross Knowledge Distillation	Jul 19, 2022	Contrastive LearningFederated Learning	CodeCode Available	1	5
Adaptive Graph Contrastive Learning for Recommendation	May 18, 2023	Collaborative FilteringContrastive Learning	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