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

Patent metadata

Field	Value
Publication number	US-12039449-B2
Application number	US-202017119381-A
Country	US
Kind code	B2
Filing date	Dec 11, 2020
Priority date	Feb 5, 2020
Publication date	Jul 16, 2024
Grant date	Jul 16, 2024

Abstract

A processor-implemented neural network method includes: 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 first class and a second class; determining, by a feature sample generator of the neural network, 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; and training a class vector of the second class included in a classifier of the neural network based on the additional feature vector and the plurality of training feature vectors of the second class.

First claim

What is claimed is:

1. 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 first class and a second class;
determining, by a feature sample generator of the neural network, 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; and
training a class vector of the second class included in a classifier of the neural network based on the additional feature vector and the plurality of training feature vectors of the second class,
wherein the determining comprises:
combining the mean vector of the first class, the variation vector of the first class, the mean vector of the second class, and the variation vector of the second class;
outputting a compressed variation vector from a result of the combining; and
determining the additional feature vector based on the compressed variation vector and the mean vector of the second class.

2. The method of claim 1 , wherein the training comprises training 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 corresponding to the second class with respect to a distance between a class vector of each of the plurality of classes and the feature vector of the training input data.

3. The method of claim 2 , wherein
the training comprises training the class vector of the second class using a loss function, and
the loss function includes a ratio of an exponential value of a negative distance of a correct answer class to a summation of exponential values of respective negative distances of the plurality of classes.

4. The method of claim 3 , wherein the loss function includes a softmax function.

5. The method of claim 1 , wherein the variation vector includes either one of a variance vector and a standard deviation vector of the plurality of training feature vectors.

6. The method of claim 5 , wherein
the mean vector is a vector of an elementwise mean value of the plurality of training feature vectors,
the variance vector is a vector of an elementwise variance of the plurality of training feature vectors, and
the standard deviation vector is a vector of an elementwise standard deviation of the plurality of training feature vectors.

7. The method of claim 1 , wherein the determining of the additional feature vector based on the compressed variation vector and the mean vector of the second class comprises determining the additional feature vector using a Gaussian probability distribution based on the compressed variation vector and the mean vector of the second class.

8. The method of claim 7 , wherein the determining of the additional feature vector based on the compressed variation vector and the mean vector of the second class comprises:
generating a plurality of feature vectors using the Gaussian probability distribution; and
determining the additional feature vector by sampling the generated plurality of feature vectors.

9. A non-transitory computer-readable storage medium storing instructions that, when executed by one or more processors, configure the one or more processors to perform the method of claim 1 .

10. 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 with respect to a distance between a class vector of each of the plurality of classes including the fourth class and the feature vector of the training input data.

11. 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,
wherein the one or more processors are further configured to:
extract, by the feature extractor, a feature vector of received input data; and
recognize, by the classifier, a class of the received input data as the second class, based on the trained class vector of the second class.

12. 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 with respect to a distance between a class vector of each of the plurality of classes including the fourth class and the feature vector of the training input data.

13. 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,
wherein the training of the neural

Assignees

• Samsung Electronics Co Ltd
• Korea Advanced Inst Sci & Tech

Inventors

• Park Seong-Jin
• Hwang Sung Ju
• Han Seungju
• Kim Insoo
• Baek Jiwon
• Han Jaejoon

Classifications

• G06N3/09

  Supervised learning · CPC title

• G06N3/0985 Primary

  Hyperparameter optimisation; Meta-learning; Learning-to-learn · CPC title

• G06N3/0499

  Feedforward networks · CPC title

• G06N3/047

  Probabilistic or stochastic networks · CPC title

• G06F18/213

  Feature extraction, e.g. by transforming the feature space; Summarisation; Mappings, e.g. subspace methods · CPC title