Abstract: An method comprises predicting a depth map by inputting a first image or a second image paired with the first image to a first machine learning model, predicting relative movement and rotation of an image capturing device using a second machine learning model, and generating a first converted image in which image conversion based on the predicted relative movement and rotation and the depth map is applied to the first image. The method also comprises calculating a loss including an error between the first converted image and the second image, and repeatedly predicting the relative movement and rotation by inputting a gradient of the loss, the first converted image, and the first image and the second image to the second machine learning model, and converging the loss including the error between the new first converted image and the second image.

Abstract: A learning apparatus executes processing of: a first neural network that extracts a first feature of a target in image data; a second neural network that extracts a second feature of the target in the image data using a network structure different from the first neural network; and a learning support neural network that extracts a third feature from the first feature extracted by the first neural network. Here, the second feature and the third feature are biased features for the target. The learning apparatus trains the learning support neural network so that the second feature and the third feature come closer, and trains the first neural network so that the third feature appearing in the first feature extracted by the first neural network is reduced.