Summary
2023
Session Number:A4L-3
Session:
Number:A4L-33
Multi-Domain Translation from Few Data by CycleGAN Applying Data Augmentation
Kanzaki Shuhei, Nakano Hidehiro,
pp.169-172
Publication Date:2023-09-21
Online ISSN:2188-5079
Summary:
In machine learning and deep learning, a huge amount of data is required for training. The image generation model GAN exists as a method to supplement the huge amount of training data. Data Augmentation is one of the methods to increase the number of data. It has been shown that the application of Data Augmentation to GANs can improve the performance of the GANs. This research proposes a method to apply Data Augmenta-tion to CycleGAN, which uses two GANs, and the effec-tiveness of the method on the model is verified.