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

arXiv: https://arxiv.org/abs/2011.09330 · HTML: https://mlsanity.com/paper/2011.09330/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 18 Nov 2020.

## Built on by (most cited first, 3 of 3)

- [QS-Attn: Query-Selected Attention for Contrastive Learning in I2I Translation](https://mlsanity.com/paper/2203.08483/) (2022)
- [Multiple GAN Inversion for Exemplar-based Image-to-Image Translation](https://mlsanity.com/paper/2103.14471/) (2021)
- [LoKO: Low-Rank Kalman Optimizer for Online Fine-Tuning of Large Models](https://mlsanity.com/paper/2410.11551/) (2024)

## Abstract (arXiv)

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.