Apparatus and method with classification

A processor-implemented classification training method, the method comprising:

• determining, using a trained first classification model, a first probability vector including a first probability, for each of a plurality of classes, resulting from a classification of an input with respect to the classes, wherein one of the classes corresponds to a truth class of the input;
• determining, based on the determined first probability vector, whether one or more of the classes represented in the first probability vector are confusing classes;
• adjusting, in response to one or more the classes being the confusing classes, the determined first probability vector based on a first probability of each of the confusing classes and a first probability of the truth class;
• and
• training a second classification model based on the adjusted first probability vector and the input.

A classification apparatus comprising:

• one or more processors configured to:
• determine a first probability vector including a first probability, for each of a plurality of classes, resulting from a classification of an input with respect to the classes;
• determine, based on the determined first probability vector, whether one or more of the classes represented in the first probability vector are confusing classes;
• adjust, in response to one or more of the classes being the confusing classes, the determined first probability vector based on a first probability of each of the confusing classes and a maximum value of the first probabilities;
• determine a second probability vector including a second probability, for each of the classes, resulting from another classification of the input with respect to the classes;
• and
• perform classification on the input based on a result of a comparison between the determined second probability vector and the adjusted first probability vector.

A processor-implemented classification method, the method comprising:

• determining a first probability that an input corresponds to a class and another first probability that the input corresponds to another class;
• increasing the other first probability, in response to the first probability being a maximum of the first probabilities and being below a threshold;
• determining a second probability that the input corresponds to the other class;
• and
• classifying the input as corresponding to the class in response to a result of a comparison between the increased first probability to the second probability being below another threshold.