Information processing apparatus, method, and non-transitory computer-readable medium for task inference using machine learning with gradient based multi-task learning

An information processing method comprising:

• data acquisition processing of acquiring, by at least one processor, data to be input to a first layer included in a plurality of layers forming a learned model generated by multi-task learning;
• inference result acquisition processing of acquiring, by the at least one processor, an inference result of a subtask, the inference result being output from a second layer, which is a layer subsequent to the first layer, by inputting the data to the first layer;
• subtask label acquisition processing of acquiring, by the at least one processor, a subtask label corresponding to the data;
• gradient calculation processing of calculating, by the at least one processor, a gradient in the data of a function using, as inputs, the inference result of the subtask and the subtask label;
• and
• inference processing of performing, by the at least one processor, task inference using the data, the gradient, and the learned model.

A non-transitory computer-readable medium storing a program that causes a computer to execute:

• a data acquisition process of acquiring data to be input to a first layer included in a plurality of layers forming a learned model generated by multi-task learning;
• an inference result acquisition process of acquiring an inference result of a subtask, the inference result being output from a second layer, which is a layer subsequent to the first layer, by inputting the data to the first layer;
• a subtask label acquisition process of acquiring a subtask label corresponding to the data;
• a gradient calculation process of calculating a gradient in the data of a function using, as inputs, the inference result of the subtask and the subtask label;
• and
• an inference process of performing task inference using the data, the gradient, and the learned model.