Computationally Efficient Approaches for Image Style Transfer
Computer Vision and Pattern Recognition
2018-07-17 v1
Abstract
In this work, we have investigated various style transfer approaches and (i) examined how the stylized reconstruction changes with the change of loss function and (ii) provided a computationally efficient solution for the same. We have used elegant techniques like depth-wise separable convolution in place of convolution and nearest neighbor interpolation in place of transposed convolution. Further, we have also added multiple interpolations in place of transposed convolution. The results obtained are perceptually similar in quality, while being computationally very efficient. The decrease in the computational complexity of our architecture is validated by the decrease in the testing time by 26.1%, 39.1%, and 57.1%, respectively.
Cite
@article{arxiv.1807.05927,
title = {Computationally Efficient Approaches for Image Style Transfer},
author = {Ram Krishna Pandey and Samarjit Karmakar and A G Ramakrishnan},
journal= {arXiv preprint arXiv:1807.05927},
year = {2018}
}