Electronic apparatus and generating method of target domain

Patent metadata

Field	Value
Publication number	US-2023106136-A1
Application number	US-202217875937-A
Country	US
Kind code	A1
Filing date	Jul 28, 2022
Priority date	Oct 6, 2021
Publication date	Apr 6, 2023
Grant date	—

Abstract

An electronic apparatus generating a generative adversarial network (GAN)-based target domain and a generating method thereof is provided. The generating method includes reconstructing source data included in a source domain, generating target data by training the reconstructed source data based on the source data, and generating a target domain including the generated target data, and the training comprises identifying at least one loss value between a class loss value by class loss and a distance loss value by distance matrix loss and applying at least one loss value between the identified class loss value and the distance loss value to the reconstructed source data.

First claim

What is claimed is:

1 . A generating method of a generative adversarial network (GAN)-based target domain, the generating method comprising:
reconstructing source data included in a source domain; generating target data by training the reconstructed source data based on the source data; and generating the target domain including the generated target data, wherein the generating of the target data comprises identifying at least one loss value of a class loss value by class loss or a distance loss value by distance matrix loss, and applying at least one loss value of the identified class loss value or the identified distance loss value to the reconstructed source data.

2 . The method of claim 1 ,
wherein the generating of the target data comprises:
based on a category of a class for the reconstructed source data corresponding to the source data being matched based on a class including the source data among a plurality of classes, identifying a first class loss value according to a preset method, and
based on the category of the class for the reconstructed source data not being matched, identifying a second class loss value according to the preset method to obtain a second class loss value, and
wherein the second class loss value is greater than the first class loss value.

3 . The method of claim 1 , wherein the generating of the target data further comprises:
identifying a distance map based on a distance between feature vectors of source data included in different classes among a plurality of classes; and obtaining the distance loss value so as to maintain a distance between the feature vectors of the reconstructed source data corresponding to the source data based on the identified distance map.

4 . The method of claim 1 , wherein the generating of the target data further comprises:
identifying at least one loss value among a cluster loss value by cluster loss, a class activating mapping (CAM) loss value by CAM loss, or a feature loss value by feature loss; and additionally applying at least one loss value among the identified cluster loss value, CAM loss value, or feature loss value to the reconstructed source data.

5 . The method of claim 4 , wherein the generating of the target data further comprises obtaining a cluster loss value based on a preset method so that a distance of feature vectors of the reconstructed source data included in different classes among a plurality of classes is far apart.

6 . The method of claim 4 , wherein the generating of the target data further comprises:
identifying a weight region of the source data to be applied when classifying a class of the source data by an artificial intelligence neural network model including the source domain; and obtaining a CAM loss value to set the weight region of the reconstructed source data corresponding to the identified source data.

7 . The method of claim 6 , wherein the weight region comprises at least one region of a specific region of image data or a specific frequency region of signal data.

8 . The method of claim 4 , wherein the generating of the target data further comprises obtaining the feature loss value so that a feature vector of the source data is the same as a feature vector of the reconstructed source data corresponding to the source data.

9 . The method of claim 1 ,
wherein the source domain is a domain generated in an artificial intelligence learning model of a first electronic apparatus, wherein the target domain is a domain for an artificial intelligence learning model of a second electronic apparatus, and wherein the first electronic apparatus and the second electronic apparatus have at least one different hardware specification, software platform, or software version.

10 . The method of claim 1 ,
wherein the generating of the target data further comprises:
receiving Gaussian distribution, and
generating fake data, and
wherein the method further comprises:
receiving trained real data and the generated fake data, and
discriminating whether input data is the real data or the fake data.

11 . The method of claim 10 , wherein the fake data is generated to be close to the real data.

12 . An electronic apparatus generating a generative adversarial network (GAN)-based target domain, the electronic apparatus comprising:
an input interface; and a processor, wherein the processor is configured to:
receive source data included in a source domain through the input interface,
reconstruct source data included in the source domain,
generate a target domain by training the reconstructed source data based on the received source data,
generate a target domain including the generated target data,
identify at least one loss value of a class loss value by class loss or a distance loss value by distance matrix loss, and
apply at least one loss value of the identified class loss value or the identified distance loss value to the reconstructed source data.

13 . The electronic apparatus of claim 12 , wherein the processor is further configured to:
based on a category of a class for the reconstructed source data corresponding to the source data being matched based on a class including the source data among a plurality of classes, identify a first class loss value according to a preset method; and based on the category of the class for the reconstructed source data not being matched, identify a second class loss value according to the preset method to obtain a second class loss value, wherein the second class loss value is greater than the first class loss value.

14 . The electronic apparatus of claim 12 , wherein the processor is further configured to:
identify a distance map based on a distance between feature vectors of source data included in different classes among a plurality of classes; and obtain the distance loss value so as to maintain a distance between the feature vectors of the reconstructed source data corresponding to the source data based on the identified distance map.

15 . The electronic apparatus of claim 12 , wherein the processor is further configured to obtain a cluster loss value based on a preset method so that a distance of feature vectors of the reconstructed source data included in different classes among a plurality of classes is far apart.

16 . The electronic apparatus of claim 12 , wherein the processor is further configured to:
identify at least one loss value among a cluster loss value by cluster loss, a class activating mapping (CAM) loss value by CAM loss, or a feature loss value by feature loss; and additionally apply at least one loss value among the identified cluster loss value, CAM loss value, or feature loss value to the reconstructed source data.

17 . The electronic apparatus of claim 16 , wherein the processor is further configured to:
identify a weight region of the source data to be applied when classifying a class of the source data by an artificial intelligence neural network model including the source domain; and obtain a CAM loss value to set the weight region of the reconstructed source data corresponding to the identified source data.

Assignees

• Samsung Electronics Co Ltd

Inventors

• Kim Sangyeon
• Lee Hyunwoo
• Han Jonghee

Classifications

• G06N3/08

  Learning methods · CPC title

• G06N3/045 Primary

  Combinations of networks · CPC title

• G06N3/0454 Primary

  Physics · mapped topic

• G06N3/0475

  Generative networks · CPC title

• G06N3/047

  Probabilistic or stochastic networks · CPC title