Neural network recogntion and training method and apparatus

A processor implemented recognition method comprising:

• inputting input data respectively to each of a first neural network portion of a neural network trained using a first loss function and a second neural network portion of the neural network trained using a second loss function;
• respectively normalizing each of an output of the first neural network portion and an output of the second neural network portion based on a reference level;
• obtaining a weighted average of the normalized output of the first neural network portion and the normalized output of the second neural network portion;
• and
• indicating a recognition result of the neural network based on the obtained weighted average.

A neural network apparatus comprising:

• a processor configured to:
• input input data to a neural network;
• determine respective mapping functions corresponding to a multiclass output of the neural network in association with the input data, including determining a mapping function of a first class and a mapping function of a second class;
• acquire a result of a loss function including a first probability component that changes correspondingly to a function value of the mapping function of the first class and a second probability component that changes contrastingly to a function value of the mapping function of the second class;
• determine a gradient of loss corresponding to the input data based on the result of the loss function;
• and
• update a parameter of the neural network based on the determined gradient of loss for generating a trained neural network based on the updated parameter.