Method, system and computer-readable medium
OwnerYasin BAYZIDIinventor
Life of this patent
Estimating the term…
Abstract
A method for assessing the reliability of a prediction made by a deep neural network includes inputting input data to a trained deep neural network, determining a class probability score of the detection, wherein the class probability score is determined by fitting of at least two multiple outlier detection methods, determining whether the input data is or is not an outlier, comparing the class probability score to a threshold value, and outputting that the prediction is or is not reliable. Also disclosed is a system and a computer readable storage medium.
Claims20
A system for assessing reliability of a detection made by a deep neural network, the system comprising:
- a processor;
- and
- a data storage, wherein the data storage comprises instructions which, when executed by the processor, cause the processor to:
- input input data from a sensor to a trained deep neural network, wherein the trained deep neural network outputs a detection based on a highest class score,
- determine a class probability score of the detection by fitting of at least two multiple outlier detection methods, wherein the at least two multiple outlier detection methods produce the class probability score,
- determine, based on the class probability score, whether the input data is an outlier by comparing the class probability score to a threshold value, and
- output an output indicating whether the detection is reliable in response to the determination of whether the input data is an outlier.
The computer-implemented method of claim 8, wherein the subsets of the input data are selected by feature bagging.
A computer-implemented method for assessing the reliability of a detection made by a deep neural network, the method comprising:
- inputting input data from a sensor to a trained deep neural network;
- using the trained deep neural network to output a detection based on a highest class score;
- determining a class probability score of the detection by fitting of at least two multiple outlier detection methods, wherein the at least two multiple outlier detection methods produce the class probability score;
- determining, based on the class probability score, whether the input data is an outlier by comparing the class probability score to a threshold value;
- outputting an output indicating whether the detection is reliable in response to the determination of whether the input data is an outlier.
Dependencies are inferred from the claim text.