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 3981–3990 of 6661 papers

Title	Date	Tasks	Status
Contrastive News and Social Media Linking using BERT for Articles and Tweets across Dual Platforms	Dec 11, 2023	ArticlesContrastive Learning	—Unverified
Contrastive Multi-view Subspace Clustering of Hyperspectral Images based on Graph Convolutional Networks	Dec 11, 2023	ClusteringContrastive Learning	—Unverified
Adaptive Feature Selection for No-Reference Image Quality Assessment by Mitigating Semantic Noise Sensitivity	Dec 11, 2023	Contrastive Learningfeature selection	—Unverified
RCA-NOC: Relative Contrastive Alignment for Novel Object Captioning	Dec 11, 2023	Contrastive LearningObject	—Unverified
Temporal Supervised Contrastive Learning for Modeling Patient Risk Progression	Dec 10, 2023	Contrastive LearningData Augmentation	CodeCode Available
CLeaRForecast: Contrastive Learning of High-Purity Representations for Time Series Forecasting	Dec 10, 2023	Contrastive LearningRepresentation Learning	—Unverified
AT4CTR: Auxiliary Match Tasks for Enhancing Click-Through Rate Prediction	Dec 9, 2023	Click-Through Rate PredictionCollaborative Filtering	—Unverified
NoiseCLR: A Contrastive Learning Approach for Unsupervised Discovery of Interpretable Directions in Diffusion Models	Dec 8, 2023	Contrastive LearningImage Generation	—Unverified
Understanding Community Bias Amplification in Graph Representation Learning	Dec 8, 2023	Contrastive LearningData Augmentation	—Unverified
Unsupervised Social Event Detection via Hybrid Graph Contrastive Learning and Reinforced Incremental Clustering	Dec 8, 2023	ClusteringContrastive Learning	CodeCode Available

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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