US 12,430,894 B2
Image processing apparatus and image processing method
Bo Wang, Yokohama Kanagawa (JP); Youyang Ng, Yokohama Kanagawa (JP); Yuchieh Lin, Yokohama Kanagawa (JP); Kengo Nakata, Kawasaki Kanagawa (JP); and Takeshi Fujiwara, Yokohama Kanagawa (JP)
Assigned to Kioxia Corporation, Tokyo (JP)
Filed by Kioxia Corporation, Tokyo (JP)
Filed on Sep. 12, 2022, as Appl. No. 17/942,815.
Claims priority of application No. 2022-038361 (JP), filed on Mar. 11, 2022.
Prior Publication US 2023/0290125 A1, Sep. 14, 2023
Int. Cl. G06V 10/774 (2022.01); G06V 10/40 (2022.01); G06V 10/764 (2022.01); G06V 10/776 (2022.01); G06V 10/82 (2022.01); G06V 20/70 (2022.01)
CPC G06V 10/7747 (2022.01) [G06V 10/40 (2022.01); G06V 10/764 (2022.01); G06V 10/776 (2022.01); G06V 10/82 (2022.01); G06V 20/70 (2022.01)] 20 Claims
OG exemplary drawing
 
1. An image processing apparatus comprising:
a first image acquisitor configured to acquire a source image to which a label is assigned;
a second image acquisitor configured to acquire a first target image to which no label is assigned;
a label acquisitor configured to acquire a label;
a feature extractor including a first neural network configured to extract a feature of the source image and a feature of the first target image;
a class classifier including a second neural network configured to perform a class classification of the source image and the first target image based on a plurality of the features extracted by the feature extractor;
a domain classifier including a third neural network configured to perform a domain classification of the source image and the first target image based on the feature extracted by the feature extractor;
a processor configured to assign a pseudo label to the first target image using the class classifier including the second neural network in a middle of learning;
a self-learner configured to perform a self-learning of the first neural network, the second neural network, and the third neural network based on a feature obtained by inputting the first target image to which the pseudo label is assigned to the feature extractor; and
a learner configured to learn the first neural network, the second neural network, and the third neural network by performing a back propagation process based on a classification result by the class classifier, a classification result by the domain classifier, and a self-learning result by the self-learner.