A learning device comprising: acquisition means for acquiring input data in which an identification target is recorded, in the input data, different forms of the same target being respectively recorded, the input data being an optical image of the target, an image generated from sensing data by a synthetic aperture radar, SAR, or sound data, the forms being an direction, an angle, a position, a size, a degree of distortion, a hue, clarity, an azimuth angle, a depression angle, when the input data is an image; and being a frequency, a recording place, an echo level, a tone, a reproduction speed of data, a noise level, a type of object generating sound, a person generating a sound, when the input data is sound data; encode means for encoding the input data to a first feature value which is a latent variable vector by using a neural network; conversion means for converting the first feature value into a second feature value which is a latent variable by using aa conversion function and a conversion parameter, the conversion function being a function shifting an arrangement of components of the first feature value based on the conversion parameter having a value according to a difference in the forms; classification means for performing classification on the second feature value; and parameter updating means for updating a value of a classification parameter used in classification by the classification means, in such a way that the classification means outputs a result indicating a class associated with the identification target as a classification to reduce an error of output data from the classification means with respect to correct answer data in which the target indicated by the input data is recorded in a form different from the forms in the input data, by comparing the output data and the correct answer data; wherein correct answer information is information to be output when a target indicated in input data associated with the correct answer information is correctly identified. acquisition means for acquiring input data in which an identification target is recorded, in the input data, different forms of the same target being respectively recorded, the input data being an optical image of the target, an image generated from sensing data by a synthetic aperture radar, SAR, or sound data, the forms being an direction, an angle, a position, a size, a degree of distortion, a hue, clarity, an azimuth angle, a depression angle, when the input data is an image; and being a frequency, a recording place, an echo level, a tone, a reproduction speed of data, a noise level, a type of object generating sound, a person generating a sound, when the input data is sound data; encode means for encoding the input data to a first feature value which is a latent variable vector by using a neural network; conversion means for converting the first feature value into a second feature value which is a latent variable by using aa conversion function and a conversion parameter, the conversion function being a function shifting an arrangement of components of the first feature value based on the conversion parameter having a value according to a difference in the forms; classification means for performing classification on the second feature value; and parameter updating means for updating a value of a classification parameter used in classification by the classification means, in such a way that the classification means outputs a result indicating a class associated with the identification target as a classification to reduce an error of output data from the classification means with respect to correct answer data in which the target indicated by the input data is recorded in a form different from the forms in the input data, by comparing the output data and the correct answer data; wherein correct answer information is information to be output when a target indicated in input data associated with the correct answer information is correctly identified.