MULTI-TASK FUSION NEURAL NETWORK ARCHITECTURE

A method includes identifying, by at least one processor, multiple features of input data using a common feature extractor. The method also includes processing, by the at least one processor, at least some identified features using each of multiple pre-processing branches. Each pre-processing branch includes a first set of neural network layers and generates initial outputs associated with a different one of multiple data processing tasks. The method further includes combining, by the at least one processor, at least two initial outputs from at least two pre-processing branches to produce combined initial outputs. In addition, the method includes processing, by the at least one processor, at least some initial outputs or at least some combined initial outputs using each of multiple post-processing branches. Each post-processing branch includes a second set of neural network layers and generates final outputs associated with a different one of the multiple data processing tasks.