1.5k citations · 2.1k across the 26 of their papers we have counts for
4 papers · 1 filter
Strike (with) a Pose: Neural Networks Are Easily Fooled by Strange Poses of Familiar Objects
Michael A. Alcorn, Qi Li, Zhitao Gong +4
Despite excellent performance on stationary test sets, deep neural networks (DNNs) can fail to generalize to out-of-distribution (OoD) inputs, including natural, non-adversarial on…
Improving Adversarial Robustness by Encouraging Discriminative Features
Chirag Agarwal, Anh Nguyen, Dan Schonfeld
Deep neural networks (DNNs) have achieved state-of-the-art results in various pattern recognition tasks. However, they perform poorly on out-of-distribution adversarial examples i.…
VectorDefense: Vectorization as a Defense to Adversarial Examples
Vishaal Munusamy Kabilan, Brandon Morris, Anh Nguyen
Training deep neural networks on images represented as grids of pixels has brought to light an interesting phenomenon known as adversarial examples. Inspired by how humans reconstr…
The Surprising Creativity of Digital Evolution: A Collection of Anecdotes from the Evolutionary Computation and Artificial Life Research Communities
Joel Lehman, Jeff Clune, Dusan Misevic +50
Biological evolution provides a creative fount of complex and subtle adaptations, often surprising the scientists who discover them. However, because evolution is an algorithmic pr…