CRPGAN: Learning image-to-image translation of two unpaired images by cross-attention mechanism and parallelization strategy
Science
Q
R
02 engineering and technology
Visual Perception
Image Processing, Computer-Assisted
0202 electrical engineering, electronic engineering, information engineering
Medicine
Humans
Animals
Learning
Translations
Horses
Seasons
Algorithms
Research Article
DOI:
10.1371/journal.pone.0280073
Publication Date:
2023-01-06T18:33:55Z
AUTHORS (6)
ABSTRACT
Unsupervised image-to-image translation (UI2I) tasks aim to find a mapping between the source and the target domains from unpaired training data. Previous methods can not effectively capture the differences between the source and the target domain on different scales and often leads to poor quality of the generated images, noise, distortion, and other conditions that do not match human vision perception, and has high time complexity. To address this problem, we propose a multi-scale training structure and a progressive growth generator method to solve UI2I task. Our method refines the generated images from global structures to local details by adding new convolution blocks continuously and shares the network parameters in different scales and also in the same scale of network. Finally, we propose a new Cross-CBAM mechanism (CRCBAM), which uses a multi-layer spatial attention and channel attention cross structure to generate more refined style images. Experiments on our collected Opera Face, and other open datasets Summer↔Winter, Horse↔Zebra, Photo↔Van Gogh, show that the proposed algorithm is superior to other state-of-art algorithms.
SUPPLEMENTAL MATERIAL
Coming soon ....
REFERENCES (54)
CITATIONS (4)
EXTERNAL LINKS
PlumX Metrics
RECOMMENDATIONS
FAIR ASSESSMENT
Coming soon ....
JUPYTER LAB
Coming soon ....