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 3661–3670 of 6661 papers

Title	Date	Tasks	Status	Hype
Towards Understanding the Mechanism of Contrastive Learning via Similarity Structure: A Theoretical Analysis	Apr 1, 2023	Contrastive LearningRepresentation Learning	—Unverified	0
HaLP: Hallucinating Latent Positives for Skeleton-based Self-Supervised Learning of Actions	Apr 1, 2023	Action RecognitionContrastive Learning	CodeCode Available	0
Weakly-Supervised Text-driven Contrastive Learning for Facial Behavior Understanding	Mar 31, 2023	Contrastive LearningFacial Expression Recognition	—Unverified	0
Simple Contrastive Representation Learning for Time Series Forecasting	Mar 31, 2023	Contrastive LearningRepresentation Learning	CodeCode Available	1
Dynamic Conceptional Contrastive Learning for Generalized Category Discovery	Mar 30, 2023	Contrastive LearningFine-Grained Visual Recognition	CodeCode Available	1
Soft Neighbors are Positive Supporters in Contrastive Visual Representation Learning	Mar 30, 2023	Contrastive Learningimage-classification	—Unverified	0
Reliable Representations Learning for Incomplete Multi-View Partial Multi-Label Classification	Mar 30, 2023	ClassificationContrastive Learning	—Unverified	0
Mixed Autoencoder for Self-supervised Visual Representation Learning	Mar 30, 2023	Contrastive LearningData Augmentation	CodeCode Available	1
Dual Circle Contrastive Learning-Based Blind Image Super-Resolution	Mar 29, 2023	Contrastive LearningImage Super-Resolution	—Unverified	0
ContraSim -- Analyzing Neural Representations Based on Contrastive Learning	Mar 29, 2023	Contrastive Learning	CodeCode Available	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