NaturalInversion: Data-Free Image Synthesis Improving Real-World
  Consistency
Yujin Kim Dogyun Park Dohee Kim Suhyun Kim
We introduce NaturalInversion, a novel model inversion-based method to
synthesize images that agrees well with the original data distribution without
using real data. In NaturalInversion, we propose: (1) a Feature Transfer
Pyramid which uses enhanced image prior of the original data by combining the
multi-scale feature maps extracted from the pre-trained classifier, (2) a
one-to-one approach generative model where only one batch of images are
synthesized by one generator to bring the non-linearity to optimization and to
ease the overall optimizing process, (3) learnable Adaptive Channel Scaling
parameters which are end-to-end trained to scale the output image channel to
utilize the original image prior further. With our NaturalInversion, we
synthesize images from classifiers trained on CIFAR-10/100 and show that our
images are more consistent with original data distribution than prior works by
visualization and additional analysis. Furthermore, our synthesized images
outperform prior works on various applications such as knowledge distillation
and pruning, demonstrating the effectiveness of our proposed method.