  In real-world applications of machine learning, reliable and safe systems
must consider measures of performance beyond standard test set accuracy. These
other goals include out-of-distribution (OOD) robustness, prediction
consistency, resilience to adversaries, calibrated uncertainty estimates, and
the ability to detect anomalous inputs. However, improving performance towards
these goals is often a balancing act that today's methods cannot achieve
without sacrificing performance on other safety axes. For instance, adversarial
training improves adversarial robustness but sharply degrades other classifier
performance metrics. Similarly, strong data augmentation and regularization
techniques often improve OOD robustness but harm anomaly detection, raising the
question of whether a Pareto improvement on all existing safety measures is
possible. To meet this challenge, we design a new data augmentation strategy
utilizing the natural structural complexity of pictures such as fractals, which
outperforms numerous baselines, is near Pareto-optimal, and roundly improves
safety measures.
