2024/02/19 by Sagar Saxena, Saxena, Sagar, Mohammad Nayeem Teli +1
Computer Science · #Computer Vision and Pattern Recognition (cs.CV) #FOS: Computer and information sciences #Generative Adversarial Networks and Image Synthesis #Machine Learning (cs.LG) #Multimodal Machine Learning Applications
paper · pdf · doi:10.48550/arxiv.2402.12531
openalex publication_date 2024/02/19 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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.