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Invertible Residual Networks and a Novel Perspective on Adversarial Examples
Joern-Henrik Jacobsen

Fri Jun 14 03:30 PM -- 04:10 PM (PDT) @
in The Third Workshop On Tractable Probabilistic Modeling (TPM) »

In this talk, I will discuss how state-of-the-art discriminative deep networks can be turned into likelihood-based density models. Further, I will discuss how such models give rise to an alternative viewpoint on adversarial examples. Under this viewpoint adversarial examples are a consequence of excessive invariances learned by the classifier, manifesting themselves in striking failures when evaluating the model on out of distribution inputs. I will discuss how the commonly used cross-entropy objective encourages such overly invariant representations. Finally, I will present an extension to cross-entropy that, by exploiting properties of invertible deep networks, enables control of erroneous invariances in theory and practice.

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Author Information

Joern-Henrik Jacobsen (Vector Institute and University of Toronto)

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