Progressive resizing allows for faster training by starting with smaller images, significantly reducing compute time. This method teaches networks essential features like edges and textures before fine-tuning with larger images. Additionally, pre-training both the critic and generator in GANs using simpler approaches has led to quicker and more reliable training processes.
