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US 12,731,223 B2Granted
Information processing apparatus, information processing method, and storage medium
AssigneeCanon Kabushiki Kaisha
InventorSho Saito
Life of this patent
Estimating the term…
Abstract
There is provided with an information processing apparatus. A performing unit performs inference on an input using a first machine learning model. A selecting unit selects a second machine learning model, in which at least some of components of a network structure of the first machine learning model have been changed, as a machine learning model used for the inference, in response to a predetermined condition being satisfied. The first machine learning model has a first component and a second component. The second machine learning model has at least a component in which a parameter or a path between nodes in the first component or the second component has been changed.
Claims14
An information processing apparatus comprising:
- at least one processor;
- and
- a memory coupled to the at least one processor, the memory storing instructions that, when executed by the at least one processor, cause the at least one processor to:
- perform inference on an input using a first machine learning model;
- and
- select a second machine learning model, in which at least some of components of a network structure of the first machine learning model have been changed, as a machine learning model used for the inference, in response to a predetermined condition being satisfied,
- wherein the first machine learning model has a first component and a second component,
- wherein the second machine learning model has at least a component in which a parameter or a path between nodes in the first component or the second component has been changed,
- wherein the first machine learning model includes a first inference unit having a first parameter and a second parameter different from the first parameter, as the first component, and a second inference unit that takes an output of the first inference unit as an input, as the second component,
- wherein the second machine learning model includes a third inference unit in which the second parameter in the first inference unit has been replaced with the first parameter, and a fourth inference unit having a same network structure as the second inference unit and taking an output of the third inference unit as an input,
- wherein the first machine learning model includes a first inference unit that outputs a first output from first inference processing and a second output from second inference processing, as the first component, and a second inference unit that takes the first output and the second output as inputs, as the second component,
- wherein the second machine learning model includes a fifth inference unit that outputs a first output from first inference processing, and a sixth inference unit that takes the first output and the second output that has been output previously as inputs,
- wherein the inference is processing for restoring a degraded image that is input, and
- wherein the degraded image is an image degraded by noise, compression, low resolution, blur, aberration, a defect, or a drop in contrast.
11 more elements
An information processing method comprising:
- performing inference on an input using a first machine learning model;
- and
- selecting a second machine learning model, in which at least some of components of a network structure of the first machine learning model have been changed, as a machine learning model used for the inference, in response to a predetermined condition being satisfied,
- wherein the first machine learning model has a first component and a second component,
- wherein the second machine learning model has at least a component in which a parameter or a path between nodes in the first component or the second component has been changed,
- wherein the first machine learning model includes a first inference unit having a first parameter and a second parameter different from the first parameter, as the first component, and a second inference unit that takes an output of the first inference unit as an input, as the second component,
- wherein the second machine learning model includes a third inference unit in which the second parameter in the first inference unit has been replaced with the first parameter, and a fourth inference unit having a same network structure as the second inference unit and taking an output of the third inference unit as an input,
- wherein the first machine learning model includes a first inference unit that outputs a first output from first inference processing and a second output from second inference processing, as the first component, and a second inference unit that takes the first output and the second output as inputs, as the second component,
- wherein the second machine learning model includes a fifth inference unit that outputs a first output from first inference processing, and a sixth inference unit that takes the first output and the second output that has been output previously as inputs,
- wherein the inference is processing for restoring a degraded image that is input, and
- wherein the degraded image is an image degraded by noise, compression, low resolution, blur, aberration, a defect, or a drop in contrast.
8 more elements
A non-transitory computer readable storage medium storing program that, when executed by a computer causes the computer to perform an information processing method comprising:
- performing inference on an input using a first machine learning model;
- and
- selecting a second machine learning model, in which at least some of components of a network structure of the first machine learning model have been changed, as a machine learning model used for the inference, in response to a predetermined condition being satisfied,
- wherein the first machine learning model has a first component and a second component,
- wherein the second machine learning model has at least a component in which a parameter or a path between nodes in the first component or the second component has been changed,
- wherein the first machine learning model includes a first inference unit having a first parameter and a second parameter different from the first parameter, as the first component, and a second inference unit that takes an output of the first inference unit as an input, as the second component,
- wherein the second machine learning model includes a third inference unit in which the second parameter in the first inference unit has been replaced with the first parameter, and a fourth inference unit having a same network structure as the second inference unit and taking an output of the third inference unit as an input,
- wherein the first machine learning model includes a first inference unit that outputs a first output from first inference processing and a second output from second inference processing, as the first component, and a second inference unit that takes the first output and the second output as inputs, as the second component,
- wherein the second machine learning model includes a fifth inference unit that outputs a first output from first inference processing, and a sixth inference unit that takes the first output and the second output that has been output previously as inputs,
- wherein the inference is processing for restoring a degraded image that is input, and
- wherein the degraded image is an image degraded by noise, compression, low resolution, blur, aberration, a defect, or a drop in contrast.
8 more elements
Dependencies are inferred from the claim text.