Learning device, learning method, and storage medium
AssigneeNEC Corporation
InventorsToshinori Araki, Kazuya Kakizaki, Inderjeet Singh
Life of this patent
Estimating the term…
Abstract
A learning device for a neural network uses the base data group to update a parameter value of the partial network and a parameter value of the normalization layer associated with the entire base data group, and uses each group of adversarial examples of each adversarial example generation condition to update the parameter value of the partial network and the parameter value of the normalization layer associated with the condition. The neural network includes a partial network, a normalization layer associated with the entirety of a base data group including a plurality of data, and a normalization layer associated with each condition of adversarial example generation.
Claims9
A learning device comprising:
- at least one memory configured to store instructions;
- and
- at least one processor configured to execute the instructions to:
- acquire a base data group, which is a group including a plurality of data;
- for each condition of adversarial example generation, use the data included in the base data group to acquire an adversarial data group, which is a group that includes two or more adversarial examples generated under that condition;
- and
- for a neural network that includes a partial network, a normalization layer associated with the entire base data group, and a normalization layer associated with each condition of adversarial example generation, each of these normalization layers normalizing the data input to the normalization layer itself using an average value and a variance value set for each normalization layer, update a parameter value of the partial network and a parameter value of the normalization layer associated with the entire base data group by using the base data group, and update the parameter value of the partial network and the parameter value of the normalization layer associated with the condition under which the adversarial example included in that adversarial data group is generated by using each adversarial data group.
A learning method executed by a computer comprises:
- acquiring a base data group, which is a group containing a plurality of data;
- using, for each condition of adversarial example generation, data included in the base data group to acquire an adversarial data group, which is a group that includes two or more adversarial examples generated under that condition;
- and
- for a neural network that includes a partial network, a normalization layer associated with the entire base data group, and a normalization layer associated with each condition of the generation of the adversarial example, each of these normalization layers normalizing the data input to the normalization layer itself using an average value and a variance value set for each normalization layer, updating a parameter value of the partial network and a parameter value of the normalization layer associated with the entire base data group by using the base data group, and updating the parameter value of the partial network and the parameter value of the normalization layer associated with the condition under which the adversarial example in that adversarial data group is generated by using each adversarial data group.
A non-transitory storage medium storing a program for causing a computer to execute:
- acquiring a base data group, which is a group containing a plurality of data;
- using, for each condition of adversarial example generation, data included in the base data group to acquire an adversarial data group, which is a group that includes two or more adversarial examples generated under that condition;
- and
- for a neural network that includes a partial network, a normalization layer associated with the entire base data group, and a normalization layer associated with each condition of the generation of the adversarial example, each of these normalization layers normalizing the data input to the normalization layer itself using an average value and a variance value set for each normalization layer, updating a parameter value of the partial network and a parameter value of the normalization layer associated with the entire base data group by using the base data group, and updating the parameter value of the partial network and the parameter value of the normalization layer associated with the condition under which the adversarial example in that adversarial data group is generated by using each adversarial data group.
Dependencies are inferred from the claim text.