Prediction of classification of an unknown input by a trained neural network

A computer system for classifying an unknown object by a trained neural network, the computer system comprising:

• a memory configured to store a plurality of programmable instructions;
• and
• a processing device in communication with the memory, wherein the processing device, upon execution of the plurality of programmable instructions is configured to:
• receive unknown input data that is not classified;
• or each class of a set of candidate classes for which the neural network has been trained:
• retrain the neural network from its trained state until a prediction can be made for classifying the input data to the class by an inference procedure;
• and
• determine an amount of effort exerted for retraining the class;
• select a class of the group of classes for which the least amount of effort was exerted;
• and
• output the selected class as the predicted class to which the input data is predicted to be classified.

A non-transitory computer readable storage medium and one or more computer programs embedded therein, the computer programs comprising instructions, which when executed by a computer system, cause the computer system to:

• receive unknown input data that is not classified;
• for each class of a set of candidate classes for which the neural network has been trained:
• retrain the neural network from its trained state until a prediction can be made for classifying the input data to the class by an inference procedure;
• and
• determine an amount of effort exerted for retraining the class;
• select a class of the group of classes for which the least amount of effort was exerted;
• and
• output the selected class as the predicted class to which the input data is predicted to be classified.