US 12,468,936 B2
Method and device with reservoir management for neural network online learning
Seonmin Rhee, Seoul (KR); Chris Dongjoo Kim, Seoul (KR); Gunhee Kim, Seoul (KR); Jinseo Jeong, Seoul (KR); and Seungju Han, Seoul (KR)
Assigned to SAMSUNG ELECTRONICS CO., LTD., Suwon-si (KR); and SNU R&DB FOUNDATION, Seoul (KR)
Filed by SAMSUNG ELECTRONICS CO., LTD., Suwon-si (KR); and SNU R&DB FOUNDATION, Seoul (KR)
Filed on Jun. 15, 2021, as Appl. No. 17/348,678.
Claims priority of application No. 10-2020-0084775 (KR), filed on Jul. 9, 2020; and application No. 10-2020-0166007 (KR), filed on Dec. 1, 2020.
Prior Publication US 2022/0012588 A1, Jan. 13, 2022
Int. Cl. G06N 3/08 (2023.01); G06N 3/04 (2023.01)
CPC G06N 3/08 (2013.01) [G06N 3/04 (2013.01)] 21 Claims
OG exemplary drawing
 
1. A processor-implemented reservoir management method, comprising:
determining, based on a sampling probability of an input data which label information is mapped, the sampling probability of the input data is based on an occurrence frequency of each class observed up to a current point in time in a data stream, a target memory allocation size for each class, and a weight for each class, whether to add the input data to a reservoir;
in response to determining to add the input data to the reservoir when the reservoir is filled, selecting candidate data to be removed from among sets of sample data included in the reservoir based on a target label distribution and a current label distribution of the reservoir, and removing the selected candidate data from the reservoir;
training a neural network model using sample data of the reservoir from which the selected candidate data is removed; and
generating a recognition result indicating a classification, identity, or presence of an object within an image data or speech data using the trained neural network model.