Open AccessUnsupervised Image to Image Translation with Additional Mask
Author(s) -
Hyun-Tae Choi,
Bong-Soo Sohn,
Byung-Woo Hong
Publication year - 2023
Publication title -
ieee access
Language(s) - English
Resource type - Journals
ISSN - 2169-3536
DOI - 10.1109/access.2023.3322146
Subject(s) - aerospace , bioengineering , communication, networking and broadcast technologies , components, circuits, devices and systems , computing and processing , engineered materials, dielectrics and plasmas , engineering profession , fields, waves and electromagnetics , general topics for engineers , geoscience , nuclear engineering , photonics and electrooptics , power, energy and industry applications , robotics and control systems , signal processing and analysis , transportation
With the development of deep learning, the performance of image-to-image translation is also increasing. However, most of the image-to-image translation models depend on the implicit method which does not explain why the models alter specific parts of the original input images. In this work, we assume that we can control the extent to which the models translate the input images using an explicit method. We explicitly create masks that will be added to the input images, aiming to highlight the difference between the inputs and the translated images. Since limiting the area of the masks directly affects the shape of the translated images, we can adjust the model through a simple regularization parameter. Our proposed method demonstrates that a simple regularization parameter, which regularizes the generated masks, can control where the model needs to change and remain. Furthermore, by adjusting the degree of the regularization parameter, we can generate diverse translated images from one original image.