Existing techniques for model inversion typically rely on hard-to-tune regularizers, such as total variation or feature regularization, which must be individually calibrated for each network in order to produce adequate images. In this work, we introduce Plug-In Inversion, which relies on a simple set of augmentations and does not require excessive hyper-parameter tuning. Under our proposed augmentation-based scheme, the same set of augmentation hyper-parameters can be used for inverting a wide range of image classification models, regardless of input dimensions or the architecture. We illustrate the practicality of our approach by inverting Vision Transformers (ViTs) and Multi-Layer Perceptrons (MLPs) trained on the ImageNet dataset, tasks which to the best of our knowledge have not been successfully accomplished by any previous works.

翻译：模型转换的现有技术通常依赖于难以调试的正规化者,如完全变异或特征正规化,而每个网络必须单独校准,才能产生适当的图像。在这项工作中,我们引入了插图转换,该插图依赖于简单的增强系统,不需要过大的超参数调试。根据我们提议的基于增强系统的计划,同一套增强超参数可用于颠倒广泛的图像分类模型,而不论输入的尺寸或结构。我们通过颠倒在图像网络数据集方面受过培训的视觉变异器和多激光倍视器(MLPs)来说明我们的方法的实用性,而我们以前的任何工作都未能最成功地完成这些任务。