System and method for improving robustness of pretrained systems in deep neural networks utilizing randomization and sample rejection

Patent metadata

Field	Value
Publication number	US-12205349-B2
Application number	US-202217698556-A
Country	US
Kind code	B2
Filing date	Mar 18, 2022
Priority date	Mar 18, 2022
Publication date	Jan 21, 2025
Grant date	Jan 21, 2025

Abstract

A system includes a machine-learning network. The network includes an input interface configured to receive input data from a sensor. The processor is programmed to receive the input data, generate a perturbed input data set utilize the input data, wherein the perturbed input data set includes perturbations of the input data, denoise the perturbed input data set utilizing a denoiser, wherein the denoiser is configured to generate a denoised data set, send the denoised data set to both a pre-trained classifier and a rejector, wherein the pre-trained classifier is configured to classify the denoised data set and the rejector is configured to reject a classification of the denoised data set, train, utilizing the denoised input data set, the a rejector to achieve a trained rejector, and in response to obtaining the trained rejector, output an abstain classification associated with the input data, wherein the abstain classification is ignored for classification.

First claim

What is claimed is:

1. A computer-implemented method for training a machine-learning network, comprising:
receiving an input data from a sensor, wherein the input data is indicative of image, radar, sonar, or sound information;
generating a perturbed input data set utilizing the input data, wherein the perturbed input data set includes perturbed data;
denoising, via a pre-trained denoiser, the perturbed input data set to generate a denoised data set;
training the machine-learning network utilizing the denoised data set, wherein the machine-learning network is configured to reject the denoised data set, utilizing a rejector, when a classification probability falls below a classification threshold, wherein the classification threshold is associated with classification the denoised data set, wherein the rejector is trained to discriminate between correctly classified inputs and misclassified denoised inputs; and
in response to the classification probability falling below the classification threshold, outputting an abstain classification associated with the input data, wherein the abstain classification is ignored for classifying.

2. The computer-implemented method of claim 1 , wherein the method includes outputting a final classification that has a majority vote in comparison to the abstain classification when classifying.

3. The computer-implemented method of claim 1 , wherein the method includes utilizing a classifier that includes a per-class rejector that is a binary detector for original classes associated with the input data.

4. The computer-implemented method of claim 1 , wherein the method includes utilizing a pre-trained classifier configured to classify the input data and a rejector configured to reject classification of the denoised data set.

5. The computer-implemented method of claim 1 , wherein the method includes utilizing multiple iterations of the denoised data set.

6. The computer-implemented method of claim 1 , wherein method further includes utilizing a classifier configured to classify the input data.

7. A system including a machine-learning network, comprising:
an input interface configured to receive input data from a sensor, wherein the sensor includes a camera, a radar, a sonar, or a microphone;
a processor, in communication with the input interface, wherein the processor is programmed to:
receive the input data, wherein the input data is indicative of image, radar, sonar, or sound information;
generate a perturbed input data set utilizing the input data, wherein the perturbed input data set includes perturbations of the input data;
denoise the perturbed input data set utilizing a denoiser, wherein the denoiser is configured to generate a denoised data set;
send the denoised data set to both a pre-trained classifier and a rejector, wherein the pre-trained classifier is configured to classify the denoised data set and the rejector is configured to reject a classification of the denoised data set, wherein the rejector is configured to be trained via discriminating correctly classified images and misclassified randomly perturbed images;
train, utilizing the denoised input data set, the a rejector to achieve a trained rejector; and
in response to obtaining the trained rejector, output an abstain classification associated with the input data, wherein the abstain classification is ignored for classifying.

8. The system of claim 7 , wherein the denoiser is a pretrained denoiser.

9. The system of claim 7 , wherein the processor is further programmed to output a final classification associated with the input data.

10. The system of claim 7 , wherein training the rejector includes utilizing Monte Carlo sampling associated with the input data.

11. The system of claim 7 , wherein the rejector includes a shared backbone configured to be parameterized.

12. The system of claim 7 , wherein the denoiser is configured to remove or mitigate Gaussian noise added to the input.

13. A computer-program product storing instructions on a non-transitory storage media which, when executed by a computer, cause the computer to:
receive input data from a sensor, wherein the input data is indicative of image, radar, sonar, or sound information;
generate a perturbed input data set utilizing the input data, wherein the perturbed input data set includes perturbations of the input data;
denoise the input data set utilizing a pretrained denoiser, wherein the pretrained denoiser is configured to generate a denoised data set;
classify or reject the denoised data set utilizing a pretrained classifier and a rejector;
train the rejector utilizing the denoised data set, wherein the rejector is configured to reject the denoised data set when a classification probability falls below classification threshold, wherein the classification threshold is associated with classification of the denoised data set, wherein the rejector is trained to discriminate between correctly classified inputs and misclassified denoised inputs; and
in response to the classification probability falling below the classification threshold, output an abstain classification associated with the input data, wherein the abstain classification ignores a classification.

14. The computer-program product of claim 13 , wherein the input data includes an image received from a camera in communication with the computer.

15. The computer-program product of claim 13 , wherein instructions further cause the computer to train the rejector, wherein training includes an upper bound and lower bound defined utilizing Monte Carlo sampling.

16. The computer-program product of claim 13 , wherein the instructions further cause the computer to classify or reject the denoised data set utilizing the pretrained classifier for multiple iterations.

17. The computer-program product of claim 13 , wherein the pretrained denoiser is configured to be trained and mitigate Gaussian noise or remove Gaussian noise.

18. The computer-program product of claim 13 , wherein parameters of the rejector are configured to learn via training the rejector to discriminate a correct classification versus a misclassification.

19. The computer-program product of claim 13 , wherein the input data includes sound information obtained from a microphone.

Assignees

• Bosch Gmbh Robert
• Univ Carnegie Mellon

Inventors

• Sheikholeslami Fatemeh
• Lin Wan-Yi
• Metzen Jan Hendrik
• Zhang Huan
• Kolter Jeremy

Classifications

• G06T5/70 Primary

  Denoising; Smoothing · CPC title

• G06V10/84

  using probabilistic graphical models from image or video features, e.g. Markov models or Bayesian networks · CPC title

• G06T2207/20084

  Artificial neural networks [ANN] · CPC title

• G06T2207/20081

  Training; Learning · CPC title

• G06T2207/20076

  Probabilistic image processing · CPC title