Learning apparatus, estimation apparatus, learning method, and non-transitory storage medium
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.
1 . 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.
2 . The learning apparatus according to claim 1 , wherein the at least one processor configured to execute the instructions to control a display apparatus to display the first captured image that is categorized as the first category by the first learning model.
3 . The learning apparatus according to claim 2 , wherein the displayed first captured image is categorized as the first category with reliability equal to or higher than a predetermined level in the first learning model.
4 . The learning apparatus according to claim 1 , wherein the determination is based on an input by a user via a display apparatus that displays the first captured image.
5 . The learning apparatus according to claim 1 , wherein the first image is accumulated previously, wherein the second image is determined as the first category by comparing with the first image.
6 . The learning apparatus according to claim 1 , wherein the first category is anomaly and the second category is normal.
7 . The learning apparatus according to claim 6 ,
wherein the at least one processor configured to execute the instructions to generate the first learning model by performing supervised learning based on a plurality of first images categorized as the first category and a plurality of second images categorized as the second category, and
wherein the plurality of first images comprises images identified by a user as anomaly.
8 . 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.
9 . The learning method according to claim 8 , further comprising controlling a display apparatus to display the first captured image that is categorized as the first category by the first learning model.
10 . The learning method according to claim 9 , wherein the displayed first captured image is categorized as the first category with reliability equal to or higher than a predetermined level in the first learning model.
11 . The learning method according to claim 8 , wherein the determination is based on an input by a user via a display apparatus that displays the first captured image.
12 . The learning method according to claim 8 , wherein the first category is anomaly and the second category is normal.
13 . The learning method according to claim 12 ,
wherein the generating the first learning model comprises performing supervised learning based on a plurality of first images categorized as the first category and a plurality of second images categorized as the second category, and
wherein the plurality of first images comprises images identified by a user as anomaly.
14 . 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.
15 . The non-transitory storage medium according to claim 14 , wherein the program that causes the computer to control a display apparatus to display the first captured image that is categorized as the first category by the first learning model.
16 . The non-transitory storage medium according to claim 15 , wherein the displayed first captured image is categorized as the first category with reliability equal to or higher than a predetermined level in the first learning model.
17 . The non-transitory storage medium according to claim 14 , wherein the determination is based on an input by a user via a display apparatus that displays the first captured image.
18 . The non-transitory storage medium according to claim 14 , wherein the first category is anomaly and the second category is normal.
19 . The non-transitory storage medium according to claim 18 ,
wherein the program further causes the computer to generate the first learning model by performing supervised learning based on a plurality of first images categorized as the first category and a plurality of second images categorized as the second category, and
wherein the plurality of first images comprises images identified by a user as anomaly.