PFC-UNIT: UNSUPERVISED IMAGE-TO-IMAGE TRANSLATION WITH PRE-TRAINED FINE-GRAINED CLASSIFICATION

Unsupervised image-to-image translation has gained great attention in data augmentation by allowing the translation of images from one domain to another while preserving their content and style. However, existing methods face major challenges when these two domains have substantial dis- discrepancies in shape and appearance. To overcome these challenges, we introduce a novel framework that can boost the naturalness and diversity of unsupervised image-to-image translation with pre-trained fine-grained classification (PFC- UNIT). Specifically, PFC-UNIT trains a content encoder to obtain the coarse-level content feature in the first stage. In the second stage, a new pre-trained fine-grained classification (PFC) is designed to generate fine-level images with style consistency. Furthermore, during the latter part of the second stage, a dynamic skip connection is added to generate finer-level images with content consistency. Experimental results show that as a plug-and-play tool, our PFC dramatically enhances the image translation effect by maintaining vivid details and keeping content and style consistent. And the proposed PFC-UNIT outperforms leading state-of-the-art methods.