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Total Style Transfer with a Single Feed-Forward Network.
- Source :
- Sensors (14248220); Jun2022, Vol. 22 Issue 12, pN.PAG-N.PAG, 17p
- Publication Year :
- 2022
-
Abstract
- The development of recent image style transfer methods allows the quick transformation of an input content image into an arbitrary style. However, these methods have a limitation that the scale-across style pattern of a style image cannot be fully transferred into a content image. In this paper, we propose a new style transfer method, named total style transfer, that resolves this limitation by utilizing intra/inter-scale statistics of multi-scaled feature maps without losing the merits of the existing methods. First, we use a more general feature transform layer that employs intra/inter-scale statistics of multi-scaled feature maps and transforms the multi-scaled style of a content image into that of a style image. Secondly, we generate a multi-scaled stylized image by using only a single decoder network with skip-connections, in which multi-scaled features are merged. Finally, we optimize the style loss for the decoder network in the intra/inter-scale statistics of image style. Our improved total style transfer can generate a stylized image with a scale-across style pattern from a pair of content and style images in one forwarding pass. Our method achieved less memory consumption and faster feed-forwarding speed compared with the recent cascade scheme and the lowest style loss among the recent style transfer methods. [ABSTRACT FROM AUTHOR]
- Subjects :
- COMPUTER vision
DEEP learning
Subjects
Details
- Language :
- English
- ISSN :
- 14248220
- Volume :
- 22
- Issue :
- 12
- Database :
- Complementary Index
- Journal :
- Sensors (14248220)
- Publication Type :
- Academic Journal
- Accession number :
- 157823002
- Full Text :
- https://doi.org/10.3390/s22124612