Method and apparatus with neural network meta-training and class vector training

A processor-implemented neural network method, the method comprising:

• extracting, by a feature extractor of a neural network, a plurality of training feature vectors corresponding to a plurality of training class data of each of a plurality of classes including a third class and a fourth class;
• determining, by a feature sample generator of the neural network, an additional feature vector of the fourth class based on a mean vector and a variation vector of the plurality of training feature vectors of each of the third class and the fourth class;
• determining, by a classifier of the neural network, a class vector of the fourth class based on the additional feature vector and the plurality of training feature vectors of the fourth class;
• and
• training the feature extractor, the feature sample generator, and the classifier to reduce a distance between the class vector of the fourth class and a feature vector of training input data corresponding to the fourth class.

A neural network apparatus, comprises:

• a memory configured to store a neural network including a feature extractor, a feature sample generator, and a classifier;
• and
• one or more processors configured to:
• extract, by the feature extractor, a plurality of training feature vectors corresponding to a plurality of training class data of each of a plurality of classes including a first class and a second class,
• determine, by the feature sample generator, an additional feature vector of the second class based on a mean vector and a variation vector of the plurality of training feature vectors of each of the first class and the second class,
• determine, by the classifier, a class vector of the second class based on the additional feature vector and the plurality of training feature vectors of the second class, and
• train the class vector of the second class to reduce a distance between the class vector of the second class and a feature vector of training input data.

A neural network apparatus, comprising:

• a memory configured to store a neural network including a feature extractor, a feature sample generator, and a classifier;
• and
• one or more processors configured to:
• extract, by the feature extractor, a plurality of training feature vectors corresponding to a plurality of training class data of each of a plurality of classes including a third class and a fourth class,
• determine, by the feature sample generator, an additional feature vector of the fourth class based on a mean vector and a variation vector of the plurality of training feature vectors of each of the third class and the fourth class,
• determine, by the classifier, a class vector of the fourth class based on the additional feature vector and the plurality of training feature vectors of the fourth class, and
• train the feature extractor, the feature sample generator, and the classifier to reduce a distance between the class vector of the fourth class and a feature vector of training input data corresponding to the fourth class.

A processor-implemented training method, the method comprising:

• extracting, by a feature extractor of a neural network, a plurality of training feature vectors of training class data of a class;
• generating, by a feature sample generator of a neural network, a variation vector of the class based on the extracted training feature vectors of training class data of the class;
• generating, by the feature sample generator, an additional feature vector of another class based the variation vector of the class;
• and
• training the neural network based on the additional feature vector.