Invention Grant
- Patent Title: Targeted data augmentation using neural style transfer
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Application No.: US15633288Application Date: 2017-06-26
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Publication No.: US10318889B2Publication Date: 2019-06-11
- Inventor: Ting Xu
- Applicant: KONICA MINOLTA LABORATORY U.S.A., INC.
- Applicant Address: US CA San Mateo
- Assignee: KONICA MINOLTA LABORATORY U.S.A., INC.
- Current Assignee: KONICA MINOLTA LABORATORY U.S.A., INC.
- Current Assignee Address: US CA San Mateo
- Agency: Chen Yoshimura LLP
- Main IPC: G06K9/00
- IPC: G06K9/00 ; G06N20/00 ; G06T3/00 ; G06T11/00

Abstract:
A method for training a deep neural network (DNN) to perform a specified task with respect to images captured by a target camera, including: using an image captured by the target camera as a style target image, training a style transformer network to perform a style transformation that transforms any photorealistic input image into a transformed image that has contents of the input image, maintains photorealistic quality of the input image, and has a style that matches a style of the style target image; using the trained style transformer network to transform training image of an original training dataset into transformed training images; labeling the transformed training images with the training labels of the corresponding training image of the original training dataset, to form an augmented training dataset; and using the augmented training dataset to train the DNN to perform the specified task.
Public/Granted literature
- US20180373999A1 TARGETED DATA AUGMENTATION USING NEURAL STYLE TRANSFER Public/Granted day:2018-12-27
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