Input Encoding for Classifier Generalization

An apparatus comprising a processor, connected to a memory, operable to:

• encode original input features from at least one input sample {right arrow over (x)}S with a uniquely decodable code using an encoder E(⋅) to produce encoded input features E({right arrow over (x)}S), wherein the at least one input sample {right arrow over (x)}S comprises uncoded input features;
• feed the uncoded input features and the encoded input features E({right arrow over (x)}S) to a base model to build an encoded model;
• and
• learn a classification function {tilde over (C)}E(⋅) using the encoded model, wherein the classification function {tilde over (C)}E(⋅) learned using the encoded model is more general than that learned using the uncoded input features alone.

A non-transitory computer program product for classification generalization, the computer program product comprising a computer readable storage medium having program instructions embodied therewith, the program instructions executable by a computer to cause the computer to:

• encode original input features from at least one input sample {right arrow over (x)}S with a uniquely decodable code using an encoder E(⋅) to produce encoded input features E({right arrow over (x)}S) wherein the at least one input sample {right arrow over (x)}S comprises uncoded input features;
• feed the uncoded input features and the encoded input features E({right arrow over (x)}S) to a base model to build an encoded model;
• and
• learn a classification function {tilde over (C)}E(⋅) using the encoded model, wherein the classification function {tilde over (C)}E(⋅) learned using the encoded model is more general than that learned using the uncoded input features alone.