Abstract: A learning device is configured to comprise a learning unit, an attention part detection unit, and a data generation unit in order to enhance estimation accuracy based on a learning model with respect to various kinds of data. The learning unit executes machine learning on the basis of first learning data and generates a learning model that classifies a category of the first learning data. The attention part detection unit classifies the category of the first learning data by using the generated learning model. When performing the classification, the attention part detection unit detects, in the first learning data, a part to which the learning model pays attention. The data generation unit generates second learning data obtained by processing the attention-paid part on the basis of the proportion of the attention-paid part matching a pre-determined attention determination part to which attention should be paid.

Abstract: Provided is a learning device that can accurately exclude training data inappropriate for learning a model from training data and can learn the model. A selecting means 73 selects first training data and second training data. Using the first training data and the second training data, a second learning means 74 learns a second model for evaluating training data by machine learning. In a case where the second model has been generated at the time of learning a first model, a first learning means evaluates each of the training data by applying each of the training data to the second model, excludes training data of a prescribed evaluation, and learns the first model.