Stable and Refined Style Transfer Using Zigzag Learning Algorithm

Recently, style transfer based on the convolutional neural network has achieved remarkable results. In this paper, we extend the original neural style transfer algorithm to ameliorate the instability in the reconstruction of certain structural information, and improve the ghosting artefacts in the background of image which with low texture and homogeneous areas. For that end, we adopt zigzag learning strategy: The model parameters are optimized to an intermediate target firstly, then let the model converge to the final goal. We show the zigzag learning to multi-sample model which is fabricated from resampling the style input and to loss function that is split into two sections. And also, we demonstrate experimentally the effectiveness of the proposed algorithm and provide its theoretical analysis. Finally we show how to integrate the zigzag learning strategy in fast neural style transfer framework.