GENERATING STRONG LABELS FOR EXAMPLES LABELLED WITH WEAK LABELS

A method for generating strong labels for examples labelled with weak labels leverages an artificial neural network, or ANN, which is assumed to have been trained on a training set of examples labelled according to weak labels (e.g., classes of structural defects in images of civil engineering structures). The method processes each example of a set of test examples by performing the following operations. The trained ANN is first executed on each example to infer a weak label. Then, the method extracts explanatory features from the ANN as executed on the example. The method generates a strong label (e.g., a region boundary of the structural defect), based on the extracted explanatory features. The method subsequently prompts a user to react to one or each of the inferred weak label and the generated strong label. The response obtained is then interpreted by the method to obtain a further weak label.