Tree-based Optimization for Image-to-Image Translation with Imbalanced Datasets on the Edge

Image-to-image (I2I) translation models typically refer to a class of adversarial architectures aiming to transfer an image content from a source domain to a target domain. To increase the image quality, data augmentation techniques or collecting new samples represent valid options yet lack of diversity and overfitting may negatively impact on the final results. In this regard, several practical scenarios do not permit to include new samples, or to employ powerful hardware, due to privacy policies or insufficient financial resources, leading to use imbalanced sets of images and favoring the more populated domain. To overcome these issues, we propose a simple and effective procedure to take advantage of the combination of critical learning parameters and demonstrate that averaging weights of multiple pre-trained $\mathrm{I}2\mathrm{I}$ models is beneficial for increasing model performance, which can be optimized for edge computing without hurting the quality of synthesized images. To this end, we define a tree-based structure, including multiple $\mathrm{I}2\mathrm{I}$ translation models, that outputs a single and more reliable network. We demonstrate that this strategy increases image quality and also show that our binary-tree learning procedure has a beneficial impact on edge devices, and it can be easily applied to architectures trained on different domains.