Abstract: A model generation method includes updating, by at least one processor, a weight matrix of a first neural network model at least based on a first inference result obtained by inputting, to the first neural network model which discriminates between first data and second data generated by using a second neural network model, the first data, a second inference result obtained by inputting the second data to the first neural network model, and a singular value based on the weight matrix of the first neural network model. The model generation method also includes at least based on the second inference result, updating a parameter of the second neural network model.

Abstract: A model generation method includes updating, by at least one processor, a weight matrix of a first neural network model at least based on a first inference result obtained by inputting, to the first neural network model which discriminates between first data and second data generated by using a second neural network model, the first data, a second inference result obtained by inputting the second data to the first neural network model, and a singular value based on the weight matrix of the first neural network model. The model generation method also includes at least based on the second inference result, updating a parameter of the second neural network model.

Abstract: A flexible data editing scheme to change and modify an intermediate representation or conditional information for a portion of to-be-edited data is disclosed. One aspect of the present disclosure relates to a data editing apparatus, comprising: one or more memories; and one or more processors configured to receive a change indication to change at least a first data area of first data; generate second data by using one or more generative models and an intermediate representation for the first data area; and replace the first data area of the first data with the second data to generate third data.

Abstract: A flexible data editing scheme to change and modify an intermediate representation or conditional information for a portion of to-be-edited data is disclosed. One aspect of the present disclosure relates to a data editing apparatus, comprising: one or more memories; and one or more processors configured to receive a change indication to change at least a first data area of first data; generate second data by using one or more generative models and an intermediate representation for the first data area; and replace the first data area of the first data with the second data to generate third data.

Abstract: Embodiments are directed to accurately measuring a distance between a “true probability distribution: q” and a “probability distribution determined from a model of a generator: p” by D(x,y) of cGANs, so that a generated image may be made closer to a true image.

Abstract: Embodiments are directed to accurately measuring a distance between a “true probability distribution: q” and a “probability distribution determined from a model of a generator: p” by D(x,y) of cGANs, so that a generated image may be made closer to a true image. A method of generating an image by using a conditional generative adversarial network constituted by two neural networks which are a generator and a discriminator, in which the discriminator outputs a result obtained from an arithmetic operation using a model of the following equation: f(x,y;?):=f1(x,y;?)+f2(x;?)=yTV???(x)+???(???(x)).

Abstract: A flexible data editing scheme to change and modify an intermediate representation or conditional information for a portion of to-be-edited data is disclosed. One aspect of the present disclosure relates to a data editing apparatus, comprising: one or more memories; and one or more processors configured to receive a change indication to change at least a first data area of first data; generate second data by using one or more generative models and an intermediate representation for the first data area; and replace the first data area of the first data with the second data to generate third data.

Abstract: A model generation method includes updating, by at least one processor, a weight matrix of a first neural network model at least based on a first inference result obtained by inputting, to the first neural network model which discriminates between first data and second data generated by using a second neural network model, the first data, a second inference result obtained by inputting the second data to the first neural network model, and a singular value based on the weight matrix of the first neural network model. The model generation method also includes at least based on the second inference result, updating a parameter of the second neural network model.

Abstract: Embodiments are directed to accurately measuring a distance between a “true probability distribution: q” and a “probability distribution determined from a model of a generator: p” by D(x,y) of cGANs, so that a generated image may be made closer to a true image.