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MS Final Exam – Neale Ratzlaff

Methods for Detection and Recovery of Out-of-Distribution Examples

Deep neural networks currently comprise the backbone of applications where safety is a critical concern, i.e. autonomous driving and medical diagnostics. Unfortunately, these systems currently fail to detect out-of-distribution (OOD) inputs and can be prone to making dangerous errors when exposed to them. In addition, these same systems are vulnerable to maliciously altered inputs called adversarial examples. In response to these problems, we present two methods to handle out-of-distribution inputs, as well as resist adversarial examples. To detect OOD inputs, we introduce HyperGAN: a generative adversarial network which learns to generate all the parameters of a deep neural network. HyperGAN first transforms low dimensional noise into a latent space, which can be sampled from to obtain diverse, performant sets of parameters for a target network architecture. By sampling many sets of parameters, we form a diverse ensemble which provides a better estimate of uncertainty than standard ensembles. We show that HyperGAN can reliably detect OOD inputs as well as adversarial examples. We also present a method for recovering clean images from adversarial examples. BFNet uses a differentiable bilateral filter as a preprocessor to a neural network. The bilateral filter projects inputs back to the space of natural images, and in doing so it removes the adversarial perturbation. We show that BFNet is an effective defense in multiple attack settings, and is able to provide additional robustness when combined with other defenses.

Major Advisor: Fuxin Li
Committee: Tom Dietterich
Committee: Alan Fern
Committee: Rakesh Bobba
GCR: Geoff Hollinger

Tuesday, November 20, 2018 at 12:00pm to 2:00pm

Kelley Engineering Center, 1126
110 SW Park Terrace, Corvallis, OR 97331

Event Type

Lecture or Presentation

Event Topic


College of Engineering, Electrical Engineering and Computer Science
Contact Name

Calvin Hughes

Contact Email

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