US 11,886,490 B2
Neural network device for retrieving image and operating method thereof
Youngkyun Jang, Seoul (KR); and Namik Cho, Seoul (KR)
Assigned to SAMSUNG ELECTRONICS CO, LTD., Suwon-si (KR); and SEOUL NATIONAL UNIVERSITY R & DB FOUNDATION, Seoul (KR)
Filed by SAMSUNG ELECTRONICS CO., LTD., Suwon-si (KR)
Filed on Apr. 5, 2021, as Appl. No. 17/222,266.
Claims priority of application No. 10-2020-0041076 (KR), filed on Apr. 3, 2020; and application No. 10-2021-0016274 (KR), filed on Feb. 4, 2021.
Prior Publication US 2021/0312234 A1, Oct. 7, 2021
Int. Cl. G06F 16/55 (2019.01); G06N 3/08 (2023.01); G06N 3/04 (2023.01); G06V 40/16 (2022.01); G06F 18/214 (2023.01); G06F 18/24 (2023.01); G06V 10/764 (2022.01); G06V 10/774 (2022.01); G06V 10/82 (2022.01)
CPC G06F 16/55 (2019.01) [G06F 18/2148 (2023.01); G06F 18/24 (2023.01); G06N 3/04 (2013.01); G06N 3/08 (2013.01); G06V 10/764 (2022.01); G06V 10/774 (2022.01); G06V 10/82 (2022.01); G06V 40/172 (2022.01)] 19 Claims
OG exemplary drawing
 
1. A neural network device, comprising:
a processor that performs an operation of training a neural network;
a feature extraction module that extracts unlabeled feature vectors that corresponds to unlabeled images and labeled feature vectors that correspond to labeled images; and
wherein the processor performs first learning with respect to a plurality of codebooks by using the labeled feature vectors but not the unlabeled feature vectors, and performs second learning with respect to the plurality of codebooks by optimizing an entropy based on all of the labeled feature vectors and the unlabeled feature vectors.