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 4051–4060 of 6661 papers

Title	Date	Tasks	Status
Self-Distilled Representation Learning for Time Series	Nov 19, 2023	Contrastive LearningRepresentation Learning	—Unverified
ML-LMCL: Mutual Learning and Large-Margin Contrastive Learning for Improving ASR Robustness in Spoken Language Understanding	Nov 19, 2023	Automatic Speech RecognitionAutomatic Speech Recognition (ASR)	—Unverified
Lesion Search with Self-supervised Learning	Nov 18, 2023	Content-Based Image RetrievalContrastive Learning	—Unverified
Community-Aware Efficient Graph Contrastive Learning via Personalized Self-Training	Nov 18, 2023	Community DetectionContrastive Learning	—Unverified
FOAL: Fine-grained Contrastive Learning for Cross-domain Aspect Sentiment Triplet Extraction	Nov 17, 2023	Aspect Sentiment Triplet ExtractionContrastive Learning	—Unverified
Few-shot Message-Enhanced Contrastive Learning for Graph Anomaly Detection	Nov 17, 2023	Anomaly DetectionContrastive Learning	—Unverified
From Pretext to Purpose: Batch-Adaptive Self-Supervised Learning	Nov 16, 2023	Contrastive LearningDimensionality Reduction	—Unverified
Robust Contrastive Learning With Theory Guarantee	Nov 16, 2023	Contrastive Learning	—Unverified
Chemist-X: Large Language Model-empowered Agent for Reaction Condition Recommendation in Chemical Synthesis	Nov 16, 2023	AI AgentContrastive Learning	—Unverified
Contrastive Transformer Learning with Proximity Data Generation for Text-Based Person Search	Nov 15, 2023	Contrastive LearningCross-Modal Retrieval	CodeCode Available

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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