  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.
