Improving Deep Generative Models on Many-To-One Image-to-Image Translation
<|reference_start|>Improving Deep Generative Models on Many-To-One Image-to-Image Translation: Deep generative models have been applied to multiple applications in image-to-image translation. Generative Adversarial Networks and Diffusion Models have presented impressive results, setting new state-of-the-art results on these tasks. Most methods have symmetric setups across the different domains in a dataset. These methods assume that all domains have either multiple modalities or only one modality. However, there are many datasets that have a many-to-one relationship between two domains. In this work, we first introduce a Colorized MNIST dataset and a Color-Recall score that can provide a simple benchmark for evaluating models on many-to-one translation. We then introduce a new asymmetric framework to improve existing deep generative models on many-to-one image-to-image translation. We apply this framework to StarGAN V2 and show that in both unsupervised and semi-supervised settings, the performance of this new model improves on many-to-one image-to-image translation.<|reference_end|>
@article{saxena2024improving,
    title={Improving Deep Generative Models on Many-To-One Image-to-Image
  Translation},
    author={Sagar Saxena, Mohammad Nayeem Teli},
    journal={arXiv preprint arXiv:2402.12531},
    year={2024},
    archivePrefix={arXiv},
    eprint={2402.12531},
    primaryClass={cs.CV cs.LG}
}