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.

翻译：我们提出NaturalInversion，一种基于模型反演的新型图像合成方法，可在不使用真实数据的情况下生成与原始数据分布高度一致的图像。NaturalInversion中我们提出：（1）特征迁移金字塔，通过融合从预训练分类器中提取的多尺度特征图，利用增强的图像先验；（2）一对一生成模型，即每个生成器仅合成单批图像以引入优化过程的非线性并简化整体优化流程；（3）可学习的自适应通道缩放参数，通过端到端训练对输出图像通道进行缩放以进一步利用原始图像先验。利用NaturalInversion，我们为在CIFAR-10/100上训练的分类器合成图像，通过可视化与附加分析证明，相较于先前工作，我们的图像与原始数据分布具有更高一致性。此外，在知识蒸馏与剪枝等多种应用中，我们的合成图像性能均优于先前方法，验证了所提方法的有效性。