Meng Wang*, Yixuan Shao, Haipeng Liu
CMC-Computers, Materials & Continua, Vol.75, No.3, pp. 5229-5254, 2023, DOI:10.32604/cmc.2023.036631
Abstract In recent years, deep generative models have been successfully applied to perform artistic painting style transfer (APST). The difficulties might lie in the loss of reconstructing spatial details and the inefficiency of model convergence caused by the irreversible en-decoder methodology of the existing models. Aiming to this, this paper proposes a Flow-based architecture with both the en-decoder sharing a reversible network configuration. The proposed APST-Flow can efficiently reduce model uncertainty via a compact analysis-synthesis methodology, thereby the generalization performance and the convergence stability are improved. For the generator, a Flow-based network using Wavelet additive coupling (WAC) layers is implemented to… More >
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