Method for automatically training a model for predicting multimedia data and method for detecting anomalies based on such a model

A computer-implemented method for training a model for reconstructing multimedia data represented by at least one modality, the model being composed of a set of a plurality of different predictors for each datum modality, the training method comprising the steps of, for each datum of a training dataset containing no anomalies,

• for each datum modality:
• masking at least part of the datum modality,
• training each predictor of the set associated with said modality to compute a different prediction of the same masked datum, each predictor being specialized in one possible prediction of the masked datum among various alternatives considered normal in the context of said multimedia data,
• for each predictor, computing a distance between the prediction and a reference datum extracted from the training data,
• selecting the predictor of the set corresponding to the smallest distance,
• computing a first cost function equal to the sum, over all the modalities, of the distances corresponding to each selected predictor,
• updating the parameters of the predictors selected for each modality so as to minimize the first cost function during training of the predictors.