US 12,444,176 B2
Model generation apparatus, estimation apparatus, model generation method, and computer-readable storage medium storing a model generation program including training a model by converting training data to deliberately reduce estimation performance for improved defect detection and feature identification
Ryo Yonetani, Tokyo (JP); Atsushi Hashimoto, Tokyo (JP); and Yamato Okamoto, Tokyo (JP)
Assigned to OMRON Corporation, Kyoto (JP)
Appl. No. 17/772,161
Filed by OMRON Corporation, Kyoto (JP)
PCT Filed Nov. 6, 2020, PCT No. PCT/JP2020/041451
§ 371(c)(1), (2) Date Apr. 27, 2022,
PCT Pub. No. WO2021/100482, PCT Pub. Date May 27, 2021.
Claims priority of application No. 2019-210873 (JP), filed on Nov. 21, 2019.
Prior Publication US 2022/0406042 A1, Dec. 22, 2022
Int. Cl. G06K 9/00 (2022.01); G06V 10/40 (2022.01); G06V 10/774 (2022.01); G06V 10/776 (2022.01); G06V 10/82 (2022.01)
CPC G06V 10/7747 (2022.01) [G06V 10/40 (2022.01); G06V 10/776 (2022.01); G06V 10/82 (2022.01)] 20 Claims
OG exemplary drawing
 
1. A model generation apparatus comprising a processor coupled to a memory storing a program and configured with the program to perform operations comprising:
acquiring a plurality of learning data sets each constituted by a combination of training data, first correct answer data that indicates a first feature comprised in the training data, and second correct answer data that indicates a second feature comprised in the training data and is different from the first feature, the training data comprises image data; and
executing machine learning of a learning model that comprises a coder, a first estimator, and a second estimator, wherein the coder is configured to convert received input data into a feature amount, the first estimator is configured to accept input of an output value of the coder, and estimate a first feature comprised in the input data from the feature amount, and the second estimator is configured to accept input of the output value of the coder, and estimate a second feature comprised in the input data from the feature amount, wherein
the processor is configured with the program to perform operations such that executing the machine learning of a learning model comprises:
training the second estimator so that, with respect to each of the learning data sets, an estimation result obtained from the second estimator by giving the training data to the coder conforms to the second correct answer data;
training the coder so that, with respect to each of the learning data sets, an estimation result obtained from the second estimator by giving the training data to the coder does not conform to the second correct answer data by converting the training data into the feature amounts such that estimation performance of the second estimator is deliberately reduced;
training the coder and the first estimator so that, with respect to each of the learning data sets, an estimation result obtained from the first estimator by giving the training data to the coder conforms to the first correct answer data, and
executing adversarial learning in the coder and the second estimator by executing training in the second estimator, and executing training in the coder that has been trained such that the estimation result obtained from the second estimator does not conform to the second correct answer data and estimation performance of the second estimator is deliberately reduced, alternately and repeatedly, and
training the coder so that, with respect to each of the learning data sets, the estimation result obtained from the second estimator by giving the training data to the coder does not conform to the second correct answer data such that estimation performance of the second estimator is deliberately reduced comprises:
training the coder using dummy data that corresponds to the second correct answer data; or
training the coder using reverse gradients.