  A discriminatively trained neural net classifier can fit the training data
perfectly if all information about its input other than class membership has
been discarded prior to the output layer. Surprisingly, past research has
discovered that some extraneous visual detail remains in the logit vector. This
finding is based on inversion techniques that map deep embeddings back to
images. We explore this phenomenon further using a novel synthesis of methods,
yielding a feedforward inversion model that produces remarkably high fidelity
reconstructions, qualitatively superior to those of past efforts. When applied
to an adversarially robust classifier model, the reconstructions contain
sufficient local detail and global structure that they might be confused with
the original image in a quick glance, and the object category can clearly be
gleaned from the reconstruction. Our approach is based on BigGAN (Brock, 2019),
with conditioning on logits instead of one-hot class labels. We use our
reconstruction model as a tool for exploring the nature of representations,
including: the influence of model architecture and training objectives
(specifically robust losses), the forms of invariance that networks achieve,
representational differences between correctly and incorrectly classified
images, and the effects of manipulating logits and images. We believe that our
method can inspire future investigations into the nature of information flow in
a neural net and can provide diagnostics for improving discriminative models.
