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 451–460 of 6661 papers

Title	Date	Tasks	Status	Hype
CrossCBR: Cross-view Contrastive Learning for Bundle Recommendation	Jun 1, 2022	Contrastive LearningGraph Learning	CodeCode Available	1
Cross-Domain Graph Anomaly Detection via Anomaly-aware Contrastive Alignment	Dec 2, 2022	Anomaly DetectionContrastive Learning	CodeCode Available	1
ContIG: Self-supervised Multimodal Contrastive Learning for Medical Imaging with Genetics	Nov 26, 2021	Contrastive Learning	CodeCode Available	1
Cross-level Contrastive Learning and Consistency Constraint for Semi-supervised Medical Image Segmentation	Feb 8, 2022	Contrastive LearningImage Segmentation	CodeCode Available	1
A class-weighted supervised contrastive learning long-tailed bearing fault diagnosis approach using quadratic neural network	Sep 21, 2023	Contrastive LearningData Augmentation	CodeCode Available	1
Cross-Modal Contrastive Learning for Text-to-Image Generation	Jan 12, 2021	Contrastive LearningGenerative Adversarial Network	CodeCode Available	1
Cross-Modal Information-Guided Network using Contrastive Learning for Point Cloud Registration	Nov 2, 2023	Contrastive LearningPoint Cloud Registration	CodeCode Available	1
Cross-Modal Retrieval with Partially Mismatched Pairs	Feb 22, 2023	Contrastive LearningCross-Modal Retrieval	CodeCode Available	1
Self-Supervised Time Series Representation Learning via Cross Reconstruction Transformer	May 20, 2022	Contrastive LearningRepresentation Learning	CodeCode Available	1
Continuous Contrastive Learning for Long-Tailed Semi-Supervised Recognition	Oct 8, 2024	Contrastive LearningDensity Estimation	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