We re-define multimodality and introduce a simple approach to multimodal and arbitrary style transfer. Conventionally, style transfer methods are limited to synthesizing a deterministic output based on a single style, and there has been no work that can generate multiple images of various details, or multimodality, given a single style. In this work, we explore a way to achieve multimodal and arbitrary style transfer by injecting noise to a unimodal method. This novel approach does not require any trainable parameters, and can be readily applied to any unimodal style transfer methods with separate style encoding sub-network in literature. Experimental results show that while being able to transfer an image to multiple domains in various ways, the image quality is highly competitive with contemporary models in style transfer.
|Title of host publication||2019 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2019 - Proceedings|
|Publisher||Institute of Electrical and Electronics Engineers Inc.|
|Number of pages||5|
|Publication status||Published - 2019 May|
|Event||44th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2019 - Brighton, United Kingdom|
Duration: 2019 May 12 → 2019 May 17
|Name||ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings|
|Conference||44th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2019|
|Period||19/5/12 → 19/5/17|
Bibliographical noteFunding Information:
This work was supported by Samsung Research Funding Center of Samsung Electronics under Project Number SRFC-IT1702-08.
© 2019 IEEE.
All Science Journal Classification (ASJC) codes
- Signal Processing
- Electrical and Electronic Engineering