Abstract
Unpaired Image-to-image translation is a problem formulation where our aim is to learn a function which can convert an image of one domain into another different domain without using a paired set of examples. One of the methods to tackle this problem is CycleGAN. Even though it had remarkable success in the recent years, it still have some issues Our method enhances CycleGAN formulation by replacing the Residual block with our proposed Residual-Inception module for multi-scale feature extraction and by adding a cyclic perceptual loss for improving the quality of texture in recovered image and generating visually better results. Qualitative results are presented on horse2zebra dataset and Quantitative results on I-Haze and Rain 1200 datasets. We show both quantitative and qualitative results on 3 datasets and show that our method improves the CycleGAN method.
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Nanda, E.S., Galshetwar, V.M., Chaudhary, S. (2022). Residual Inception Cycle-Consistent Adversarial Networks. In: Raman, B., Murala, S., Chowdhury, A., Dhall, A., Goyal, P. (eds) Computer Vision and Image Processing. CVIP 2021. Communications in Computer and Information Science, vol 1568. Springer, Cham. https://doi.org/10.1007/978-3-031-11349-9_36
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