Title:
高精密度のイメージを分析するためのディープラーニングネットワークを使用するためにトレーニングイメージをオートラベリングするオートラベルリング装置のハイパーパラメータを最適化する方法、及びこれを利用した最適化装置
Document Type and Number:
Japanese Patent JP6849932
Kind Code:
B2
Abstract:
A method for optimizing a hyperparameter of an auto-labeling device performing auto-labeling and auto-evaluating of a training image to be used for learning a neural network is provided for computation reduction and achieving high precision. The method includes steps of: an optimizing device, (a) instructing the auto-labeling device to generate an original image with its auto label and a validation image with its true and auto label, to assort the original image with its auto label into an easy-original and a difficult-original images, and to assort the validation image with its own true and auto labels into an easy-validation and a difficult-validation images; and (b) calculating a current reliability of the auto-labeling device, generating a sample hyperparameter set, calculating a sample reliability of the auto-labeling device, and optimizing the preset hyperparameter set. This method can be performed by a reinforcement learning with policy gradient algorithms.
Inventors:
Kim, Kei-Hyun
Kim, Yong Jun
Kim, Insoo
Kim, haku-kyung
Nam, Woo Hyun
Boo, Soohun
Son, Munchur
Yeoh, Dong Hoon
Liu, Uju
Chang, Taeun
John, Kyun Chung
Choi, Hongo
Butterfly, hojin
Kim, Yong Jun
Kim, Insoo
Kim, haku-kyung
Nam, Woo Hyun
Boo, Soohun
Son, Munchur
Yeoh, Dong Hoon
Liu, Uju
Chang, Taeun
John, Kyun Chung
Choi, Hongo
Butterfly, hojin
Application Number:
JP2020004783A
Publication Date:
March 31, 2021
Filing Date:
January 15, 2020
Export Citation:
Assignee:
Stradvision,Inc.
International Classes:
G06N20/00; G06T7/00
Other References:
Pierre Blanchart, et al.,"A Semi-Supervised Algorithm for Auto-Annotation and Unknown Structures Discovery in Satellite Image Databases",IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing,2010年 8月 9日,Vol.3, No.4,Pages 698-717,ISSN: 1939-1404, .
Fei Wu, et al.,"Weakly Semi-Supervised Deep Learning for Multi-Label Image Annotation",IEEE Transactions on Big Data,2015年11月 4日,Vol.1, No.3,Pages 109-122,ISSN: 2332-7790,.
Martin Bauml, et al.,"Semi-supervised Learning with Constraints for Person Identification in Multimedia Data",Proceedings of the 2013 IEEE Conference on Computer Vision and Pattern Recognition,2013年 6月28日,Pages 3602-3609,ISBN: 978-1-5386-5672-3,.
Fei Wu, et al.,"Weakly Semi-Supervised Deep Learning for Multi-Label Image Annotation",IEEE Transactions on Big Data,2015年11月 4日,Vol.1, No.3,Pages 109-122,ISSN: 2332-7790,
Martin Bauml, et al.,"Semi-supervised Learning with Constraints for Person Identification in Multimedia Data",Proceedings of the 2013 IEEE Conference on Computer Vision and Pattern Recognition,2013年 6月28日,Pages 3602-3609,ISBN: 978-1-5386-5672-3,
Attorney, Agent or Firm:
Asamura patent office