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 3551–3560 of 6661 papers

Title	Date	Tasks	Status
Few-shot Message-Enhanced Contrastive Learning for Graph Anomaly Detection	Nov 17, 2023	Anomaly DetectionContrastive Learning	—Unverified
Few-Shot Nested Named Entity Recognition	Dec 2, 2022	Contrastive LearningFew-Shot Learning	—Unverified
Few-shot Object Detection with Refined Contrastive Learning	Nov 24, 2022	Contrastive LearningFew-Shot Object Detection	—Unverified
Few-shot Open Relation Extraction with Gaussian Prototype and Adaptive Margin	Oct 27, 2024	Contrastive LearningMeta-Learning	—Unverified
Few-shot Oriented Object Detection with Memorable Contrastive Learning in Remote Sensing Images	Mar 20, 2024	Contrastive LearningFew-Shot Object Detection	—Unverified
Few-Shot Point Cloud Semantic Segmentation via Contrastive Self-Supervision and Multi-Resolution Attention	Feb 21, 2023	Contrastive LearningFew-Shot Learning	—Unverified
Few-shot Single-view 3D Reconstruction with Memory Prior Contrastive Network	Jul 30, 2022	3D ReconstructionContrastive Learning	—Unverified
Few-shot Text Classification with Dual Contrastive Consistency	Sep 29, 2022	ClassificationContrastive Learning	—Unverified
Few-shot Visual Reasoning with Meta-analogical Contrastive Learning	Jul 23, 2020	Contrastive LearningLogical Reasoning	—Unverified
FewUser: Few-Shot Social User Geolocation via Contrastive Learning	Mar 28, 2024	Contrastive LearningLanguage Modelling	—Unverified

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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