Additional training apparatus, additional training method, and storage medium

An additional training method comprising:

• storing, in a memory, (i) a plurality of pieces of existing training data in which existing data is input data and a classification of defects according to the existing data is output data, and (ii) cluster data representing clusters to each of which respective pieces of the existing training data belong;
• extracting, based on the cluster data, a plurality of pieces of first existing training data from each of the clusters as a subset of the pieces of the existing training data therein such that a size ratio between the clusters after extraction is maintained, whereby the extracted first existing training data retain a distribution of the existing training data such that robust training for the existing training data can be performed;
• acquiring a plurality of pieces of new training data in which new data is input data and a classification of defects according to the new data is output data;
• and
• storing, in the memory, a plurality of pieces of additional training data that are based on the pieces of first existing training data and the pieces of new training data.

A non-transitory computer readable storage medium including computer executable instructions, wherein the instructions, when executed by a processor, cause the processor to perform a method comprising:

• storing, in a memory, (i) a plurality of pieces of existing training data in which existing data is input data and a classification of defects according to the existing data is output data, and (ii) cluster data representing clusters to each of which respective pieces of the existing training data belong;
• extracting, based on the cluster data, a plurality of pieces of first existing training data from each of the clusters as a subset of the pieces of the existing training data therein such that a size ratio between the clusters after extraction is maintained, whereby the extracted first existing training data retain a distribution of the existing training data such that robust training for the existing training data can be performed;
• acquiring a plurality of pieces of new training data in which new data is input data and a classification of defects according to the new data is output data;
• and
• storing, in the memory, a plurality of pieces of additional training data that are based on the pieces of first existing training data and the pieces of new training data.