Learning apparatus, estimation apparatus, learning method, and non-transitory storage medium
AssigneeNEC Corporation
InventorsJianquan Liu, Kenta Ishihara
Life of this patent
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Abstract
The present invention provides a learning apparatus (10) including: an acquisition unit (11) that acquires an image; a similarity computation unit (12) that computes a similarity between the acquired image, and a first image being accumulated in advance and indicating an abnormal state; a registration unit (13) that registers, as a second image indicating a normal state, the acquired image whose similarity is equal to or less than a first reference value; and a learning unit (14) that generates an estimation model for discriminating between normal and abnormal by machine learning using the first image and the second image.
Claims19
A learning apparatus comprising:
- at least one memory storing instructions;
- and
- at least one processor configured to execute the instructions to:
- generate a first learning model for discriminating between a first category and a second category by performing supervised learning based on a first image categorized as the first category and a second image categorized as the second category;
- use the first learning model to acquire a category of a first captured image as one of a plurality of categories, the plurality of categories including the first category and the second category;
- receive a determination indicating whether the acquired category is either correct or incorrect;
- generate a second learning model by performing supervised learning based on the first captured image and the determination, wherein the second learning model categorizes an image into the plurality of categories;
- and
- use the second learning model to acquire a category of a second captured image as one of the plurality of categories.
A learning method executed by a computer, the learning method comprising:
- generating a first learning model for discriminating between a first category and a second category by performing supervised learning based on a first image categorized as the first category and a second image categorized as the second category;
- using the first learning model to acquire a category of a first captured image as one of a plurality of categories, the plurality of categories including the first category and the second category;
- receiving a determination indicating whether the acquired category is either correct or incorrect;
- generating a second learning model by performing supervised learning based on the first captured image and the determination, wherein the second learning model categorizes an image into the plurality of categories;
- and
- using the second learning model to acquire a category of a second captured image as one of the plurality of categories.
A non-transitory storage medium storing a program that causes a computer to:
- generate a first learning model for discriminating between a first category and a second category by performing supervised learning based on a first image categorized as the first category and a second image categorized as the second category;
- use the first learning model to acquire a category of a first captured image as one of a plurality of categories, the plurality of categories including the first category and the second category;
- receive a determination indicating whether the acquired category is either correct or incorrect;
- generate a second learning model by performing supervised learning based on the first captured image and the determination, wherein the second learning model categorizes an image into the plurality of categories;
- and
- use the second learning model to acquire a category of a second captured image as one of the plurality of categories.
Dependencies are inferred from the claim text.