Abstract: An encoding step and an image selection step are performed by a computer 20. In the encoding step, a plurality of partial images D2 of an object are input to a learned VAE model M, and the plurality of partial images D2 are encoded to a plurality of latent variables in a multi-dimensional latent space. In the image selection step, one or more partial images D2 that are likely to include an alignment mark are selected on the basis of a distribution of the plurality of latent variables in the latent space. Thus, a partial image that is likely to include an alignment mark is automatically detected by the computer 20 with the use of the VAE model M. This can lighten a burden on an operator who searches for an alignment mark.

Abstract: A training device includes a data acquirer that acquires training data including a first image of a first type and a second image of a second type that is different from the first type, a first trainer that repeats an epoch for causing the first image and the second image to be learned under a same condition multiple times, and a second trainer that repeats an epoch for training a trained learning model generated by the first trainer with a rate of error back-propagation in regard to the first image and a rate of error back-propagation in regard to the second image being made different from each other.