LEARNING DEVICE, LEARNING METHOD, AND STORAGE MEDIUM

20250078535

18/815880

August 27, 2024

A learning device that learns a machine learning model that uses an image including a plurality of pixels as an input and outputs a determination value indicating whether or not each of the pixels represents a road, including: a storage medium that stores a computer-readable command; and a processor that is connected to the storage medium, the processor executing the computer-readable command to set a weight for an error between the determination value output by the machine learning model and learning data indicating whether or not each of the pixels represents a road and learn the machine learning model to reduce a value of a loss function calculated on the basis of the error for which the weight has been set, the processor increasing the weight in one or more predetermined directions from a lower end center of the image.

1. A learning device that learns a machine learning model that uses an image including a plurality of pixels as an input and outputs a determination value indicating whether or not each of the pixels represents a road, the learning device comprising: a storage medium that stores a computer-readable command; and a processor that is connected to the storage medium, wherein the processor executes the computer readable command to set a weight for an error between the determination value output by the machine learning model and learning data indicating whether or not each of the pixels represents a road, and learn the machine learning model to reduce a value of a loss function calculated on the basis of the error for which the weight has been set, and the processor increases the weight in one or more predetermined directions from a lower end center of the image.

2. The learning device according to claim 1, wherein the processor increases the weight toward an upper end and left and right ends from the lower end center of the image as the one or more predetermined directions.

3. The learning device according to claim 1, wherein the processor increases the weight toward an upper end from the lower end center of the image as the one or more predetermined directions.

4. A learning method of learning a machine learning model that uses an image including a plurality of pixels as an input and outputs a determination value indicating whether or not each of the pixels represents a road, the method comprising, by a computer: setting a weight for an error between the determination value output by the machine learning model and learning data indicating whether or not each of the pixels represents a road; and learning the machine learning model to reduce a value of a loss function calculated on the basis of the error for which the weight has been set, wherein in the setting, the weight is increased in one or more predetermined directions from a lower end center of the image.

5. A computer-readable non-transitory storage medium that stores a program causing a machine learning model that uses an image including a plurality of pixels as an input and outputs a determination value indicating whether or not each of the pixels represents a road to be learned, the program causes a computer to: set a weight for an error between the determination value output by the machine learning model and learning data indicating whether or not each of the pixels represents a road; and learn the machine learning model to reduce a value of a loss function calculated on the basis of the error for which the weight has been set, wherein in the setting, the weight is increased in one or more predetermined directions from a lower end center of the image.

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edspap.20250078535