Tiny Transform Net for Mobile Image Stylization

Abstract

Artistic stylization is an image transformation problem that renders an image in the style of another one. Existing methods either regard image style transfer as an optimization of perceptual loss function based on a pre-trained network, or train a feed forward network that achieves style transfer through one forward propagation. However, time-consuming optimization processes or relatively large feed forward networks are unacceptable for mobile application. In this work we propose a tiny transform net to accomplish image stylization on mobile devices. The advantages of our proposed architecture come from that: (i) The size of the carefully designed network is less than 40KB, which is more than 166 times smaller than the current popular network; (ii) Progressive training is put forward to keep the training stable, which is implemental to achieve semantics aware stylization; (iii) Deep convolutional network inference algorithm is reconstructed on mobile platform to reduce the overhead of storage and time. In addition, well-trained tiny transform nets and demo application will be made available.