Systems and methods for machine classification and learning that is robust to unknown inputs

摘要

The invention includes systems and methods, including computer programs encoded on computer storage media, for classifying inputs as belonging to a known or unknown class as well as for updating the system to improve is performance. In one system, there is a desired feature representation for unknown inputs, e.g., a zero vector, and the system includes transforming input data to produce a feature representation, using that to compute dissimilarity with the desired feature representation for unknown inputs and combining dissimilarity with other transformations of the feature representation to determine if the input is from a specific known class or if it is unknown. In one embodiment, the system transforms the magnitude of the feature representation into a confidence score. In an update method to improve performance, the system transforms inputs into feature representations which go through a scoring means and then use a robust loss function, which has different loss terms for known and unknown inputs which are then used to update the system weights to improve performance.