Diverse Single Image Generation with Controllable Global Structure though Self-Attention

• URL: http://arxiv.org/abs/2102.04780v1
• Date: Tue, 9 Feb 2021 11:52:48 GMT
• Title: Diverse Single Image Generation with Controllable Global Structure though Self-Attention
• Authors: Sutharsan Mahendren, Chamira Edussooriya, Ranga Rodrigo
• Abstract summary: We show how to generate images that require global context using generative adversarial networks. 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.
• Score: 1.2522889958051286
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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