公开(公告)号：EP4038553A1

公开(公告)日：2022-08-10

申请号：EP20872583.8

申请日：2020-10-01

申请人： Nokia Technologies Oy

发明人： CRICRI, Francesco ,  HANNUKSELA, Miska ,  AKSU, Emre, Baris ,  REZAZADEGAN TAVAKOLI, Hamed ,  RANGU, Goutham

IPC分类号： G06N3/08 ,  G06N3/04 ,  G06F9/50 ,  G06N20/00 ,  G06K9/62

公开(公告)号：EP3955166A3

公开(公告)日：2022-02-23

申请号：EP21188138.8

申请日：2021-07-28

申请人： Nokia Technologies Oy

发明人： RANGU, Goutham ,  CRICRÌ, Francesco ,  AKSU, Emre Baris

IPC分类号： G06N3/08 ,  G06N3/04 ,  G06K9/62 ,  G06V40/16

摘要： A system, obtaining a first training dataset, comprising a plurality of first image and pose data pairs; obtaining a first generated dataset, comprising a plurality of first image and estimated pose data pairs, wherein estimated pose data of the first image and estimated pose data pairs are generated by a first neural network trained using the first training dataset; obtaining a second generated dataset, comprising a plurality of second image and estimated pose data pairs, wherein estimated pose data of the second image and estimated pose data pairs are generated by a second neural network trained using the first training dataset; generating the first and second generated datasets a generated training dataset, comprising image and estimated pose data pairs selected from said first generated dataset; and training a third neural network based on a combination of some or all of the first training dataset and the generated training dataset.

公开(公告)号：EP3955166A2

公开(公告)日：2022-02-16

申请号：EP21188138.8

申请日：2021-07-28

申请人： Nokia Technologies Oy

发明人： RANGU, Goutham ,  CRICRÌ, Francesco ,  AKSU, Emre Baris

IPC分类号： G06N3/04 ,  G06N3/08 ,  G06K9/62 ,  G06V40/16

摘要： A system, obtaining a first training dataset, comprising a plurality of first image and pose data pairs; obtaining a first generated dataset, comprising a plurality of first image and estimated pose data pairs, wherein estimated pose data of the first image and estimated pose data pairs are generated by a first neural network trained using the first training dataset; obtaining a second generated dataset, comprising a plurality of second image and estimated pose data pairs, wherein estimated pose data of the second image and estimated pose data pairs are generated by a second neural network trained using the first training dataset; generating the first and second generated datasets a generated training dataset, comprising image and estimated pose data pairs selected from said first generated dataset; and training a third neural network based on a combination of some or all of the first training dataset and the generated training dataset.