Machine learning system and machine learning method

A machine learning system comprising:

• one or more processors;
• and
• a non-transitory computer-readable memory storing instructions that, when executed by the one or more processors, cause the one or more processors to:
• access, from a pretrained model repository, a plurality of pretrained models obtained by learning a transfer source task under respective conditions;
• access a transfer learning dataset related to a transfer target task, the transfer learning dataset comprising input data and teaching information;
• for each pretrained model of the plurality of pretrained models, evaluate an adaptability score of the pretrained model to the transfer learning dataset by:
• forward propagating at least a portion of the input data through the pretrained model to obtain an image segmentation result that segments the input data into a plurality of areas such that a same area has a same meaning;
• for each segmented area, obtaining a respective feature representation produced by the pretrained model for the segmented area;
• determine a correspondence between segmented areas in the image segmentation result and classes indicated by the teaching information based at least in part on an overlap degree between (A) pixels of the segmented areas and (B) pixels associated with the teaching information;
• and
• computing the adaptability score as a function representing a relation between the image segmentation result and the teaching information using at least one of:
• a probability density, an overlap degree of the segmented areas, and a similarity of the respective feature representations for the segmented areas;
• select, based on the adaptability scores, a selected pretrained model from the plurality of pretrained models;
• and
• execute transfer learning using the selected pretrained model and the transfer learning dataset to output a trained model.