A new data augmentation strategy utilizing the natural structural complexity of pictures such as fractals
Introduction
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