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 4161–4170 of 6661 papers

Title	Date	Tasks	Status	Hype
A Semi-supervised Learning Approach for B-line Detection in Lung Ultrasound Images	Nov 25, 2022	Contrastive LearningLine Detection	—Unverified	0
Global and Local Hierarchy-aware Contrastive Framework for Implicit Discourse Relation Recognition	Nov 25, 2022	Contrastive LearningMulti-Task Learning	CodeCode Available	1
Copy-Pasting Coherent Depth Regions Improves Contrastive Learning for Urban-Scene Segmentation	Nov 25, 2022	Contrastive LearningDepth Estimation	CodeCode Available	0
Cross-domain Transfer of defect features in technical domains based on partial target data	Nov 24, 2022	ClassificationContrastive Learning	—Unverified	0
Pose-disentangled Contrastive Learning for Self-supervised Facial Representation	Nov 24, 2022	Contrastive LearningData Augmentation	CodeCode Available	1
Graph Contrastive Learning for Materials	Nov 24, 2022	Contrastive Learning	—Unverified	0
Learning with Partial Labels from Semi-supervised Perspective	Nov 24, 2022	Contrastive LearningPartial Label Learning	CodeCode Available	1
Contrastive pretraining for semantic segmentation is robust to noisy positive pairs	Nov 24, 2022	Contrastive LearningSemantic Segmentation	—Unverified	0
Hierarchical Consistent Contrastive Learning for Skeleton-Based Action Recognition with Growing Augmentations	Nov 24, 2022	Action RecognitionContrastive Learning	CodeCode Available	1
Few-shot Object Detection with Refined Contrastive Learning	Nov 24, 2022	Contrastive LearningFew-Shot Object Detection	—Unverified	0

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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