Learning device, learning method, and learning program
A learning device includes processing circuitry configured to acquire data of which a label is predicted, and reduce, in a model representing a probability distribution of the label of the acquired data, a rank of a Fisher information matrix for the data to a value less than a predetermined value and learn the model.
1 . A learning device comprising:
processing circuitry configured to:
acquire training data of which a label is predicted;
generate a model, representing a probability distribution of the label of the training data, to resist an adversarial example by:
increasing a temperature in a Boltzmann distribution to a value greater than 1 in the probability distribution, and
learning the model;
receive input data of which a label is to be predicted; and
predict the label of the input data by using the learned model.
2 . The learning device of claim 1 , wherein the processing circuitry is further configured to perform the learning using the adversarial example that is generated by superimposing noise on the training data.
3 . The learning device of claim 1 , wherein the processing circuitry is further configured to perform the learning by generating the adversarial example using a first temperature value in the probability distribution and updating the model using a loss function generated with a second temperature value that is greater than the first temperature value.
4 . The learning device of claim 3 , wherein the first temperature value is 1.
5 . The learning device of claim 1 , wherein the processing circuitry is further configured to predict the label of the input data by setting a temperature value in the probability distribution to 1.
6 . The learning device of claim 1 , wherein the processing circuitry is further configured to perform the learning by performing an iterative process of generating the adversarial example and updating the model until a loss function converges.
7 . The learning device of claim 1 , wherein the processing circuitry is further configured to perform the learning by reducing a loss function that is based on a true probability of the label of the training data and a probability predicted by the model.
8 . A learning method which is executed in a learning device, the learning method comprising:
acquiring training data of which a label is predicted;
generating a model, representing a probability distribution of the label of the training data, to resist an adversarial example by:
increasing a temperature in a Boltzmann distribution to a value greater than 1 in the probability distribution, and
learning the model;
receiving input data of which a label is to be predicted; and
predicting the label of the input data by using the learned model.
9 . A non-transitory computer-readable recording medium storing therein a learning program that causes a computer to execute a process comprising:
acquiring training data of which a label is predicted;
generating a model, representing a probability distribution of the label of the training data, to resist an adversarial example by:
increasing a temperature in a Boltzmann distribution to a value greater than 1 in the probability distribution, and
learning the model;
receiving input data of which a label is to be predicted; and
predicting the label of the input data by using the learned model.