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 561–570 of 6661 papers

Title	Date	Tasks	Status	Hype
Emotion Rendering for Conversational Speech Synthesis with Heterogeneous Graph-Based Context Modeling	Dec 19, 2023	Contrastive LearningSpeech Synthesis	CodeCode Available	1
Object-Aware Domain Generalization for Object Detection	Dec 19, 2023	Autonomous DrivingContrastive Learning	CodeCode Available	1
Frequency Spectrum is More Effective for Multimodal Representation and Fusion: A Multimodal Spectrum Rumor Detector	Dec 18, 2023	Contrastive Learning	CodeCode Available	1
Generalized Category Discovery with Large Language Models in the Loop	Dec 18, 2023	Active LearningContrastive Learning	CodeCode Available	1
ContraNovo: A Contrastive Learning Approach to Enhance De Novo Peptide Sequencing	Dec 18, 2023	Contrastive Learningde novo peptide sequencing	CodeCode Available	1
SeGA: Preference-Aware Self-Contrastive Learning with Prompts for Anomalous User Detection on Twitter	Dec 17, 2023	Contrastive LearningMisinformation	CodeCode Available	1
Rethinking Dimensional Rationale in Graph Contrastive Learning from Causal Perspective	Dec 16, 2023	Contrastive LearningMeta-Learning	CodeCode Available	1
Stethoscope-guided Supervised Contrastive Learning for Cross-domain Adaptation on Respiratory Sound Classification	Dec 15, 2023	Audio ClassificationContrastive Learning	CodeCode Available	1
CETN: Contrast-enhanced Through Network for CTR Prediction	Dec 15, 2023	Click-Through Rate PredictionContrastive Learning	CodeCode Available	1
Multi-Modality is All You Need for Transferable Recommender Systems	Dec 15, 2023	AllContrastive Learning	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