Abstract: A learning data processing unit accepts, as input, a plurality of pieces of learning data for a respective plurality of tasks, and calculates, for each of the tasks, a batch size which meets a condition that a value obtained by dividing a data size of corresponding one of the pieces of learning data by the corresponding batch size is the same between the tasks. A batch sampling unit samples, for each of the tasks, samples from corresponding one of the pieces of learning data with the corresponding batch size calculated by the learning data processing unit. A learning unit updates a weight of a discriminator for each of the tasks, using the samples sampled by the batch sampling unit.

Abstract: A learning data processing unit accepts, as input, a plurality of pieces of learning data for a respective plurality of tasks, and calculates, for each of the tasks, a batch size which meets a condition that a value obtained by dividing a data size of corresponding one of the pieces of learning data by the corresponding batch size is the same between the tasks. A batch sampling unit samples, for each of the tasks, samples from corresponding one of the pieces of learning data with the corresponding batch size calculated by the learning data processing unit. A learning unit updates a weight of a discriminator for each of the tasks, using the samples sampled by the batch sampling unit.

Abstract: A first intermediate layer activity level calculator refers to an index stored in a first intermediate layer storage, acquires, from among activity levels of respective nodes in an input layer calculated by an input layer activity level calculator and weight for respective edges and bias values stored in the first intermediate layer storage, an activity level of each node in the input layer that is connected with each node in the first intermediate layer and weight for each of edges and a bias value thereof, and calculates an activity level of each node in the first intermediate layer using the activity level of each node in the first input layer and the weight for each of the edges and the bias value having been acquired. This is capable of reducing the calculation amount and the memory amount upon performing inference, and also obtaining higher inference accuracy.