  Existing techniques to solve exemplar-based image-to-image translation within
deep convolutional neural networks (CNNs) generally require a training phase to
optimize the network parameters on domain-specific and task-specific
benchmarks, thus having limited applicability and generalization ability. In
this paper, we propose a novel framework, for the first time, to solve
exemplar-based translation through an online optimization given an input image
pair, called online exemplar fine-tuning (OEFT), in which we fine-tune the
off-the-shelf and general-purpose networks to the input image pair themselves.
We design two sub-networks, namely correspondence fine-tuning and multiple GAN
inversion, and optimize these network parameters and latent codes, starting
from the pre-trained ones, with well-defined loss functions. Our framework does
not require the off-line training phase, which has been the main challenge of
existing methods, but the pre-trained networks to enable optimization in
online. Experimental results prove that our framework is effective in having a
generalization power to unseen image pairs and clearly even outperforms the
state-of-the-arts needing the intensive training phase.
