  Recent techniques to solve photorealistic style transfer within deep
convolutional neural networks (CNNs) generally require intensive training from
large-scale datasets, thus having limited applicability and poor generalization
ability to unseen images or styles. To overcome this, we propose a novel
framework, dubbed Deep Translation Prior (DTP), to accomplish photorealistic
style transfer through test-time training on given input image pair with
untrained networks, which learns an image pair-specific translation prior and
thus yields better performance and generalization. Tailored for such test-time
training for style transfer, we present novel network architectures, with two
sub-modules of correspondence and generation modules, and loss functions
consisting of contrastive content, style, and cycle consistency losses. Our
framework does not require offline training phase for style transfer, which has
been one of the main challenges in existing methods, but the networks are to be
solely learned during test-time. Experimental results prove that our framework
has a better generalization ability to unseen image pairs and even outperforms
the state-of-the-art methods.
