Online Exemplar Fine-Tuning for Image-to-Image Translation

• URL: http://arxiv.org/abs/2011.09330v1
• Date: Wed, 18 Nov 2020 15:13:16 GMT
• Title: Online Exemplar Fine-Tuning for Image-to-Image Translation
• Authors: Taewon Kang, Soohyun Kim, Sunwoo Kim, Seungryong Kim
• Abstract summary: 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. We propose a novel framework, for the first time, to solve exemplar-based translation through an online optimization given an input image pair. 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.
• Score: 32.556050882376965
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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