1. Diverse single image generation with controllable global structure
- Author
-
Mahendren, Sutharsan, Edussooriya, Chamira, and Rodrigo, Ranga
- Subjects
FOS: Computer and information sciences ,Artificial Intelligence ,Computer Vision and Pattern Recognition (cs.CV) ,Computer Science::Computer Vision and Pattern Recognition ,Cognitive Neuroscience ,Image and Video Processing (eess.IV) ,FOS: Electrical engineering, electronic engineering, information engineering ,ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION ,Computer Science - Computer Vision and Pattern Recognition ,Electrical Engineering and Systems Science - Image and Video Processing ,Computer Science Applications - Abstract
Image generation from a single image using generative adversarial networks is quite interesting due to the realism of generated images. However, recent approaches need improvement for such realistic and diverse image generation, when the global context of the image is important such as in face, animal, and architectural image generation. This is mainly due to the use of fewer convolutional layers for mainly capturing the patch statistics and, thereby, not being able to capture global statistics very well. We solve this problem by using attention blocks at selected scales and feeding a random Gaussian blurred image to the discriminator for training. Our results are visually better than the state-of-the-art particularly in generating images that require global context. The diversity of our image generation, measured using the average standard deviation of pixels, is also better., Published in the Neurocomputing Journal
- Published
- 2023