WSAM: Visual Explanations from Style Augmentation as Adversarial
  Attacker and Their Influence in Image Classification
Felipe Moreno-Vera Edgar Medina Jorge Poco
Currently, style augmentation is capturing attention due to convolutional
neural networks (CNN) being strongly biased toward recognizing textures rather
than shapes. Most existing styling methods either perform a low-fidelity style
transfer or a weak style representation in the embedding vector. This paper
outlines a style augmentation algorithm using stochastic-based sampling with
noise addition to improving randomization on a general linear transformation
for style transfer. With our augmentation strategy, all models not only present
incredible robustness against image stylizing but also outperform all previous
methods and surpass the state-of-the-art performance for the STL-10 dataset. In
addition, we present an analysis of the model interpretations under different
style variations. At the same time, we compare comprehensive experiments
demonstrating the performance when applied to deep neural architectures in
training settings.