  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.
