High-Resolution Daytime Translation Without Domain Labels
The authors propose a novel image-to-image translation model which is capable of learning on fully unsupervised data (without any domain labels, which is a major improvement over current state-of-the-art methods, namely FUNIT by NVIDIA) and an upscaling technique for generating high-resolution images while keeping scene semantics
For the generator, authors utilize resnet-like generator with skip connections and adaptive instance normalization. The key to success was the usage of two ideas:
1. Combined usage of styles, extracted from the real images, with the ones sampled from the prior distribution
2. Usage of a conditional discriminator, that takes both generated image and the style vector as an input
The enhancement network is inspired by ESRGAN and takes multiple transfer results, obtained via applying the generator to shifted and downsampled Hi-Res image.
Authors showcase their model on modeling various daytime appearances for a single given image as the main task. The model has been trained on a custom dataset of still landscape images with a varying time of day (which was unknown during training). Authors also show the versatility of the approach for artistic style transfer task, training the model on the WikiArt dataset and applying it to real photographs
Project link: https://saic-mdal.github.io/HiDT/
#gan #image2image #highresolution #cv
The authors propose a novel image-to-image translation model which is capable of learning on fully unsupervised data (without any domain labels, which is a major improvement over current state-of-the-art methods, namely FUNIT by NVIDIA) and an upscaling technique for generating high-resolution images while keeping scene semantics
For the generator, authors utilize resnet-like generator with skip connections and adaptive instance normalization. The key to success was the usage of two ideas:
1. Combined usage of styles, extracted from the real images, with the ones sampled from the prior distribution
2. Usage of a conditional discriminator, that takes both generated image and the style vector as an input
The enhancement network is inspired by ESRGAN and takes multiple transfer results, obtained via applying the generator to shifted and downsampled Hi-Res image.
Authors showcase their model on modeling various daytime appearances for a single given image as the main task. The model has been trained on a custom dataset of still landscape images with a varying time of day (which was unknown during training). Authors also show the versatility of the approach for artistic style transfer task, training the model on the WikiArt dataset and applying it to real photographs
Project link: https://saic-mdal.github.io/HiDT/
#gan #image2image #highresolution #cv
