Abstract: A learning apparatus, a method of generating a learned model, and a program are capable of adequately widening the dynamic ranges of various images in a unified fashion. A training data generating section generates, on the basis of a second-class image, a first-class image associated with the second-class image, by referring to associative data where luminance values in a second dynamic range and luminance values in a first dynamic range are associated with each other. A learning section performs a learning process of a machine learning model by using the first-class image and the second-class image associated with the first-class image.

Abstract: Provided are a message output apparatus, a learning apparatus, a message output method, a learning method, and a program that allow a user to feel as if communication is being performed. An input section inputs, to a learned machine learning model that has been learned using learning data that includes learning input data including a plurality of consecutive frames of learning images and teaching data including a message associated with the learning input data, target input data including at least a plurality of consecutive frames of target images. A message identification section identifies a message according to output produced when the target input data is input to the machine learning model. A message output section outputs the identified message.

Abstract: A second learning data acquisition section acquires an input image. A wide angle-of-view image generation section generates, in response to an input of the input image, a generated wide angle-of-view image that is an image having a wider angle of view than the input image. The second learning data acquisition section acquires a comparative wide angle-of-view image that is an image to be compared with the generated wide angle-of-view image. A second learning section performs learning for the wide angle-of-view image generation section by, on the basis of a comparison result between the generated wide angle-of-view image and the comparative wide angle-of-view image, updating parameter values of the wide angle-of-view image generation section such that, according to the luminance levels of pixels in the comparative wide angle-of-view image or the luminance levels of pixels in the generated wide angle-of-view image, update amounts of the parameter values concerning the pixels are increased.

Abstract: A learning input image acquisition section acquires a plurality of input images individually depicting a state imaged at a predetermined angle of view in a predetermined relative imaging direction. A learning wide angle-of-view image acquisition section acquires a wide angle-of-view image having an angle of view including all the angles of view of the plurality of input images. A learning section performs learning of a wide angle-of-view image generation section based on a learning wide angle-of-view image and an output that is generated when the plurality of input images are inputted to the wide angle-of-view image generation section, which generates and outputs an image having an angle of view including all the angles of view of a plurality of images in response to the input of the plurality of images.

Abstract: Provided are a message output apparatus, a learning apparatus, a message output method, a learning method, and a program that allow a user to feel as if communication is being performed. An input section inputs, to a learned machine learning model that has been learned using learning data that includes learning input data including a plurality of consecutive frames of learning images and teaching data including a message associated with the learning input data, target input data including at least a plurality of consecutive frames of target images. A message identification section identifies a message according to output produced when the target input data is input to the machine learning model. A message output section outputs the identified message.

Abstract: A learning input image acquisition section acquires a plurality of input images individually depicting a state imaged at a predetermined angle of view in a predetermined relative imaging direction. A learning wide angle-of-view image acquisition section acquires a wide angle-of-view image having an angle of view including all the angles of view of the plurality of input images. A learning section performs learning of a wide angle-of-view image generation section based on a learning wide angle-of-view image and an output that is generated when the plurality of input images are inputted to the wide angle-of-view image generation section, which generates and outputs an image having an angle of view including all the angles of view of a plurality of images in response to the input of the plurality of images.

Abstract: A second learning data acquisition section acquires an input image. A wide angle-of-view image generation section generates, in response to an input of the input image, a generated wide angle-of-view image that is an image having a wider angle of view than the input image. The second learning data acquisition section acquires a comparative wide angle-of-view image that is an image to be compared with the generated wide angle-of-view image. A second learning section performs learning for the wide angle-of-view image generation section by, on the basis of a comparison result between the generated wide angle-of-view image and the comparative wide angle-of-view image, updating parameter values of the wide angle-of-view image generation section such that, according to the luminance levels of pixels in the comparative wide angle-of-view image or the luminance levels of pixels in the generated wide angle-of-view image, update amounts of the parameter values concerning the pixels are increased.