Information processing method and apparatus

Patent metadata

Field	Value
Publication number	US-2020302287-A1
Application number	US-201916565810-A
Country	US
Kind code	A1
Filing date	Sep 10, 2019
Priority date	Mar 18, 2019
Publication date	Sep 24, 2020
Grant date	—

Abstract

According to one embodiment, an information processing method for a neural network model optimized by a training by using a processor and a memory includes: outputting a first information processing result by the neural network model using first input data; and outputting a second information processing result by the neural network model using second input data obtained by applying a perturbation to the first input data. The method further includes determining a reliability of the neural network model using the first input data based on a comparison result between the first information processing result and the second information processing result.

First claim

What is claimed is:

1 . An information processing method for a neural network model optimized by a training by using a processor and a memory for use in computation of the processor, the method comprising:
outputting a first information processing result by the neural network model using first input data; outputting a second information processing result by the neural network model using second input data obtained by applying a perturbation to the first input data; and determining a reliability of the neural network model using the first input data based on a comparison result between the first information processing result and the second information processing result.

2 . The method of claim 1 , wherein the neural network model is optimized by a training against adversarial examples used as the perturbation.

3 . The method of claim 1 , further comprising:
calculating a value indicative of a predetermined perturbation based on the first information processing result; and outputting the second input data by applying the calculated value indicative of the predetermined perturbation to the first input data.

4 . The method of claim 3 , wherein the value indicative of the predetermined perturbation makes a loss included in the first information processing result relatively small.

5 . The method of claim 1 , wherein the perturbation includes adversarial examples.

6 . The method of claim 5 , wherein the determining comprises: determining that the neural network model has a low reliability with respect to the adversarial examples, if the comparison result shows that the first information processing result and the second information processing result are different from each other.

7 . The method of claim 1 , wherein the determining comprises: determining that the neural network model outputs a correct information processing result, if the comparison result shows that the first information processing result and the second information processing result are the same.

8 . The method of claim 1 , wherein the determining comprises: determining that an event of misrecognition due to an effect of the perturbation has occurred in the neural network model, if the comparison result shows that the first information processing result and the second information processing result are different from each other.

9 . The method of claim 1 , wherein the neural network model includes a convolutional neural network;
each of the first input data and the second input data includes image data; and each of the first information processing result and the second information processing result includes an image recognition result.

10 . An information processing apparatus for a neural network model optimized by a training, the apparatus comprising:
a processor; and a memory configured to be used in processing of computation of the processor, wherein the processor is configured to:
output a first information processing result by the neural network model using first input data;
output a second information processing result by the neural network model using second input data obtained by applying a perturbation to the first input data; and
determine a reliability of the neural network model using the first input data based on a comparison result between the first information processing result and the second information processing result.

11 . The apparatus of claim 10 , wherein the neural network model is optimized by a training against adversarial examples used as the perturbation.

12 . The apparatus of claim 10 , wherein the processor is configured to:
calculate a value indicative of a predetermined perturbation based on the first information processing result; and output the second input data by applying the calculated value indicative of the predetermined perturbation to the first input data.

13 . The apparatus of claim 10 , wherein the processor is configured to determine that the neural network model outputs a correct information processing result, if the comparison result shows that the first information processing result and the second information processing result are the same.

14 . The apparatus of claim 10 , wherein the processor is configured to determine that an event of misrecognition due to an effect of the perturbation has occurred in the neural network model, if the comparison result shows that the first information processing result and the second information processing result are different from each other.

15 . The apparatus of claim 10 , wherein the processor is configured to determine that the neural network model has a low reliability with respect to adversarial examples, if the comparison result shows that the first information processing result and the second information processing result are different from each other.

16 . The apparatus of claim 10 , wherein the neural network model includes a convolutional neural network;
each of the first input data and the second input data includes image data; and each of the first information processing result and the second information processing result includes an image recognition result.

Assignees

• Toshiba Memory Corp

Inventors

• Tachibana Fumihiko

Classifications

• G06N3/084 Primary

  Backpropagation, e.g. using gradient descent · CPC title

• G06V10/7788

  the supervisor being a human, e.g. interactive learning with a human teacher · CPC title

• G06V10/776

  Validation; Performance evaluation · CPC title

• G06N3/08 Primary

  Learning methods · CPC title

• G06F18/217

  Validation; Performance evaluation; Active pattern learning techniques · CPC title