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 1191–1200 of 6661 papers

Title	Date	Tasks	Status	Hype
ConCL: Concept Contrastive Learning for Dense Prediction Pre-training in Pathology Images	Jul 14, 2022	Contrastive LearningSaliency Detection	CodeCode Available	1
Contrastive Deep Supervision	Jul 12, 2022	Contrastive LearningFine-Grained Image Classification	CodeCode Available	1
Self-supervised Group Meiosis Contrastive Learning for EEG-Based Emotion Recognition	Jul 12, 2022	Contrastive LearningData Augmentation	CodeCode Available	1
Towards Proper Contrastive Self-supervised Learning Strategies For Music Audio Representation	Jul 10, 2022	Contrastive LearningInformation Retrieval	CodeCode Available	1
Sudowoodo: Contrastive Self-supervised Learning for Multi-purpose Data Integration and Preparation	Jul 8, 2022	BlockingContrastive Learning	CodeCode Available	1
DLME: Deep Local-flatness Manifold Embedding	Jul 7, 2022	Contrastive LearningData Augmentation	CodeCode Available	1
Contrastive Learning from Spatio-Temporal Mixed Skeleton Sequences for Self-Supervised Skeleton-Based Action Recognition	Jul 7, 2022	Action RecognitionContrastive Learning	CodeCode Available	1
Network Binarization via Contrastive Learning	Jul 6, 2022	BinarizationContrastive Learning	CodeCode Available	1
Integrating multi-label contrastive learning with dual adversarial graph neural networks for cross-modal retrieval	Jul 5, 2022	Contrastive LearningCross-Modal Retrieval	CodeCode Available	1
Deep Contrastive One-Class Time Series Anomaly Detection	Jul 4, 2022	Anomaly DetectionContrastive Learning	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