Learning apparatus and learning method
AssigneeCanon Kabushiki Kaisha
InventorHiroshi Yoshikawa
Life of this patent
Estimating the term…
Abstract
A learning apparatus performs a first assignment in which M tasks that are different from each other are assigned to N neural networks (where N<M) and perform learning processing that is related to the M tasks in parallel; and determines, based on learning results of the respective M tasks, whether to assign, in subsequent learning processing, the respective M tasks to the same neural networks as in the first assignment or to neural networks different from those of the first assignment.
Claims13
A learning apparatus comprising one or more memories storing instructions and one or more processors that execute the instructions to:
- perform a first assignment in which M tasks that are different from each other are assigned to N neural networks (where N<M) and perform learning processing that is related to the M tasks in parallel;
- and
- determine, based on learning results of the respective M tasks, whether to assign, in subsequent learning processing, the respective M tasks to the same neural networks as in the first assignment or to neural networks different from those of the first assignment.
A learning method comprising:
- performing a first assignment in which M tasks that are different from each other are assigned to N neural networks (where N<M) and perform learning processing that is related to the M tasks in parallel;
- and
- determining, based on learning results of the respective M tasks, whether to assign, in subsequent learning processing, the respective M tasks to the same neural networks as in the first assignment or to neural networks different from those of the first assignment.
A non-transitory computer-readable recording medium storing a program that, when executed by a computer, causes the computer to perform a learning method comprising:
- performing a first assignment in which M tasks that are different from each other are assigned to N neural networks (where N<M) and perform learning processing that is related to the M tasks in parallel;
- and
- determining, based on learning results of the respective M tasks, whether to assign, in subsequent learning processing, the respective M tasks to the same neural networks as in the first assignment or to neural networks different from those of the first assignment.
Dependencies are inferred from the claim text.