Abstract: A signal processing apparatus executes a convolution operation of predetermined layers constituting a neural network; and transfers first form data to be stored in a storage. The apparatus executes, on output data outputted from a convolution operation of a first layer among the predetermined layers, an arithmetic operation of a compression layer that is configured by a neural network and compresses data, and outputs the first form data to be transmitted to the storage. The apparatus further executes, on the first form data stored in the storage, an arithmetic operation of a restoration layer that is configured by a neural network and restores pre-compression data, and outputs input data to be inputted to a convolution operation of a second layer among the predetermined layers.

Abstract: An apparatus that performs machine learning of a learning model that can increase a rate of outputting a block dividing pattern as a correct answer. A CPU of the apparatus determines a dividing pattern output from a learning model that outputs, using an image for learning as an input thereto, a dividing pattern obtained by dividing the image, and a weight associated with a similarity with teacher data when performing machine learning of the learning model. The CPU performs machine learning of the learning model using a loss function weighted by the determined weight.

Abstract: A learning apparatus includes an adjustment unit configured to, for a training set including a plurality of pieces of training data, adjust the number of pieces of training data included in the training set such that feature values of the plurality of pieces of training data have a predetermined distribution, and a training unit configured to perform machine learning using the training set to generate a learned model.