Method for determining a robust prediction and certification interval associated with a regression task

Abstract

Method for training an automatic prediction model of a physical quantity, comprising the steps of: Receiving (401) an initial automatic prediction model of said quantity, Receiving (402) a set of training data, Generating (403) a set of noisy training data by adding to each data item of the training data set a randomly drawn noise value, For each data item of the noisy training data set, executing (404) the initial automatic prediction model to determine a main prediction of the physical quantity, Modifying the initial model by replacing the main prediction of the physical quantity with a robustified prediction,Train (405) the modified model from the noisy training dataset such that the robustified prediction is equal to a median value of the distribution of the main prediction values provided by the initial model from the noisy training dataset. Fig. 4,

Claims

is less than the prediction of the physical quantity itself less than the prediction of the upper limit of the certification interval.

16. Method for automatically predicting a physical quantity according to any one of claims 14 or 15 comprising providing a robustness indicator of the robustified prediction to said adverse attack, the robustness indicator being inversely proportional to the width of the certification interval.

17. A method for automatically predicting a physical quantity according to any one of claims 14 to 16 wherein the physical quantity is a position of an object in an image and the training data are sets of images.

18. Device for automatic prediction of a physical quantity comprising a calculation unit configured to execute the steps of the method according to any one of claims 14 to 17 and a display interface for displaying the results of the method.

19. An automatic prediction device according to claim 18 wherein the physical quantity is a position of an object in an image and the training data are sets of images.

20. A computer program comprising code instructions for implementing one of the methods according to any one of claims 1 to 17, when said program is executed on a computer.

21. A computer-readable recording medium on which the computer program according to claim 20 is recorded. FIG.1 FIG.2

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