Understanding Invariance via Feedforward Inversion of Discriminatively Trained Classifiers

Understand

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.