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 1091–1100 of 6661 papers

Title	Date	Tasks	Status	Hype
Consistent Explanations by Contrastive Learning	Oct 1, 2021	Contrastive LearningExplainable artificial intelligence	CodeCode Available	1
Consistent Representation Learning for Continual Relation Extraction	Mar 5, 2022	Continual Relation ExtractionContrastive Learning	CodeCode Available	1
Contrast and Classify: Training Robust VQA Models	Oct 13, 2020	Contrastive LearningData Augmentation	CodeCode Available	1
CSLP-AE: A Contrastive Split-Latent Permutation Autoencoder Framework for Zero-Shot Electroencephalography Signal Conversion	Nov 13, 2023	Contrastive LearningEEG	CodeCode Available	1
Cross-Patch Dense Contrastive Learning for Semi-Supervised Segmentation of Cellular Nuclei in Histopathologic Images	Jan 1, 2022	Contrastive LearningImage Segmentation	CodeCode Available	1
Constrained Contrastive Distribution Learning for Unsupervised Anomaly Detection and Localisation in Medical Images	Mar 5, 2021	Anomaly DetectionContrastive Learning	CodeCode Available	1
Learning the Unlearned: Mitigating Feature Suppression in Contrastive Learning	Feb 19, 2024	Contrastive Learning	CodeCode Available	1
CUTS: A Deep Learning and Topological Framework for Multigranular Unsupervised Medical Image Segmentation	Sep 23, 2022	Contrastive LearningImage Segmentation	CodeCode Available	1
Correspondence Matters for Video Referring Expression Comprehension	Jul 21, 2022	Contrastive LearningReferring Expression	CodeCode Available	1
Automatic Biomedical Term Clustering by Learning Fine-grained Term Representations	Apr 1, 2022	ClusteringContrastive Learning	CodeCode Available	1

Show:10 25 50

← PrevPage 110 of 667Next →

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