Abstract: The conversion unit (132) converts first data into a first frequency component, and converts second data generated by a generator that configures an adversarial learning model into a second frequency component. The calculation unit (133) calculates a loss function that simultaneously optimizes the generator, a first discriminator that configures the adversarial learning model and discriminates between the first data and the second data, and a second discriminator that configures the adversarial learning model and discriminates between the first frequency component and the second frequency component. The update unit (134) updates parameters of the generator, the first discriminator, and the second discriminator so that the loss function calculated by the calculation unit (133) is optimized.

Abstract: An inference device executes a first conversion step of converting an output from an intermediate layer using a bounded nonlinear function in a final layer of a deep neural network having the intermediate layer and the final layer. Moreover, the inference device executes a second conversion step of converting a value obtained by conversion in the first conversion step using an activation function.

Abstract: An acquisition unit 15a acquires data in a task. The learning unit 15b learns a generation model representing a distribution of a probability of the data in the task so that a mutual information amount between a latent variable and an observed variable is minimized in the model.

Abstract: A classification device (10) includes: a classification unit (12) that performs classification by using a model (121) that is a model performing classification and is a deep learning model; and a preprocessing unit (11) that is provided prior to the classification unit (12), and selects an input to the model (121) by using a mask model (111) that minimizes a sum of a loss function and a magnitude of the input to the classification unit (12), the loss function evaluating a relationship between a label on an input from teaching data and an output of the model (121).

Abstract: A learning apparatus (10) includes: a generation unit (11) configured having a mathematical model for generating data through an input of a random number used for deep learning to a nonlinear function; and a prior learning unit (13) configured to cause the generation unit (11) to execute prior learning of a variance and an average using unscented transform (UT). The prior learning unit (13) estimates, using UT, the variance and the average of data generated by the generation unit (11) and updates a parameter of the generation unit (11) to minimize an evaluation function for evaluating a similarity between the estimated variance and average and a variance and an average of true data calculated in advance.

Abstract: A calculation unit (121) calculates, for an output signal of an output layer in a neural network, an output function obtained by replacing an exponential function included in softmax with a product of the exponential function and a predetermined function having no parameter, the output function having a non-linear log likelihood function. An update unit (122) updates a parameter of the neural network on the basis of the output signal such that the log likelihood function of the output function is optimized.