Method and information processing apparatus that perform transfer learning while suppressing occurrence of catastrophic forgetting

An information processing apparatus, comprising:

• one or more processors;
• and
• a storage device that stores a program that is executable by the one or more processors,
• wherein
• the storage device further stores weight parameters that respectively correspond to a plurality of units included in each of a plurality of layers of a neural network that have learned, in sequence, a first task to a (T−1)th task that respectively correspond to a first learning set to a (T−1)th learning set by using the first learning set to the (T−1)th learning set, where T is an integer equal to or larger than 2,
• by executing the program, the one or more processors cause the information processing apparatus to execute learning processing that causes the neural network to learn a Tth task corresponding to a Tth learning set using the Tth learning set, and
• the learning processing includes
• for each of the plurality of units included in the plurality of layers, determining an importance degree of the unit in the Tth task,
• for each of the plurality of layers, determining dissimilar tasks from among the first task to the (T−1)th task, the dissimilar tasks being not similar to the Tth task in terms of behaviors in the layer, and
• in learning that uses the Tth learning set, suppressing updating of the weight parameters of the plurality of units included in the plurality of layers in accordance with importance degrees of the dissimilar tasks that have been determined for each of the plurality of layers.

A non-transitory computer-readable storage medium that stores a program,

• wherein
• when executed by one or more processors of an information processing apparatus, the program causes the information processing apparatus to execute learning processing that causes a neural network to learn a task corresponding to a learning set, the neural network including a plurality of layers that each include a plurality of units including weight parameters, and
• in causing the neural network to learn a Tth task corresponding to a Tth learning set after the neural network has learned, in sequence, a first task to a (T−1)th task that respectively correspond to a first learning set to a (T−1)th learning set, where T is an integer equal to or larger than 2, the learning processing includes
• for each of the plurality of units in the plurality of layers, determining an importance degree of the unit in the Tth task,
• for each of the plurality of layers, determining dissimilar tasks from among the first task to the (T−1)th task, the dissimilar tasks being not similar to the Tth task in terms of behaviors in the layer, and
• in learning that uses the Tth learning set, suppressing updating of the weight parameters of the plurality of units included in the plurality of layers in accordance with importance degrees of the dissimilar tasks that have been determined for each of the plurality of layers.