Masked Discriminators for Content-Consistent Unpaired Image-to-Image Translation

التفاصيل البيبلوغرافية
العنوان: Masked Discriminators for Content-Consistent Unpaired Image-to-Image Translation
المؤلفون: Stuhr, Bonifaz, Brauer, Jürgen, Schick, Bernhard, Gonzàlez, Jordi
سنة النشر: 2023
المجموعة: Computer Science
مصطلحات موضوعية: Computer Science - Computer Vision and Pattern Recognition, Computer Science - Artificial Intelligence, Computer Science - Graphics, Computer Science - Machine Learning, I.2, I.3, I.4, I.5, I.6
الوصف: A common goal of unpaired image-to-image translation is to preserve content consistency between source images and translated images while mimicking the style of the target domain. Due to biases between the datasets of both domains, many methods suffer from inconsistencies caused by the translation process. Most approaches introduced to mitigate these inconsistencies do not constrain the discriminator, leading to an even more ill-posed training setup. Moreover, none of these approaches is designed for larger crop sizes. In this work, we show that masking the inputs of a global discriminator for both domains with a content-based mask is sufficient to reduce content inconsistencies significantly. However, this strategy leads to artifacts that can be traced back to the masking process. To reduce these artifacts, we introduce a local discriminator that operates on pairs of small crops selected with a similarity sampling strategy. Furthermore, we apply this sampling strategy to sample global input crops from the source and target dataset. In addition, we propose feature-attentive denormalization to selectively incorporate content-based statistics into the generator stream. In our experiments, we show that our method achieves state-of-the-art performance in photorealistic sim-to-real translation and weather translation and also performs well in day-to-night translation. Additionally, we propose the cKVD metric, which builds on the sKVD metric and enables the examination of translation quality at the class or category level.
Comment: 24 pages, 22 figures, under review
نوع الوثيقة: Working Paper
URL الوصول: http://arxiv.org/abs/2309.13188
رقم الأكسشن: edsarx.2309.13188
قاعدة البيانات: arXiv