Learned Image Compression With Multi-Scale Spatial and Contextual information Fusion

Many image datasets are built from web searches, with images taken by various cameras. The variance of camera sources can lead to different camera signals and colors within images of the same class, which may impede neural networks from fitting the data. To generalize neural networks to different camera sources, we propose an augmentation method using unpaired image-to-image translation to transfer training images into another camera model domain. Our approach utilizes CycleGAN to create a translation mapping between two different camera models. We show that such a mapping can be applied to any image as a form of data augmentation and is able to outperform traditional color-based transformations. Additionally, this approach can be further enhanced with geometric transformations.