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 4381–4390 of 6661 papers

Title	Date	Tasks	Status	Hype
Augmented Dual-Contrastive Aggregation Learning for Unsupervised Visible-Infrared Person Re-Identification	Oct 14, 2022	Contrastive LearningPerson Re-Identification	CodeCode Available	1
Instance Segmentation with Cross-Modal Consistency	Oct 14, 2022	Autonomous DrivingContrastive Learning	—Unverified	0
MICO: A Multi-alternative Contrastive Learning Framework for Commonsense Knowledge Representation	Oct 14, 2022	Contrastive LearningKnowledge Graph Completion	CodeCode Available	1
Robust Preference Learning for Storytelling via Contrastive Reinforcement Learning	Oct 14, 2022	Contrastive LearningLanguage Modeling	—Unverified	0
Blind Super-Resolution for Remote Sensing Images via Conditional Stochastic Normalizing Flows	Oct 14, 2022	Blind Super-ResolutionContrastive Learning	—Unverified	0
Fine-grained Category Discovery under Coarse-grained supervision with Hierarchical Weighted Self-contrastive Learning	Oct 14, 2022	Contrastive Learning	CodeCode Available	1
Invariance-adapted decomposition and Lasso-type contrastive learning	Oct 13, 2022	Contrastive LearningVocal Bursts Type Prediction	—Unverified	0
TractoSCR: A Novel Supervised Contrastive Regression Framework for Prediction of Neurocognitive Measures Using Multi-Site Harmonized Diffusion MRI Tractography	Oct 13, 2022	Contrastive LearningDiffusion MRI	—Unverified	0
LEAVES: Learning Views for Time-Series Data in Contrastive Learning	Oct 13, 2022	Contrastive LearningData Augmentation	—Unverified	0
Low-resource Neural Machine Translation with Cross-modal Alignment	Oct 13, 2022	Contrastive Learningcross-modal alignment	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