# WSAM: Visual Explanations from Style Augmentation as Adversarial Attacker and Their Influence in Image Classification Source: [WSAM: Visual Explanations from Style Augmentation as Adversarial Attacker and Their Influence in Image Classification]() Collection index: [llms.txt]() Data updated: 2025-10-27T06:25:24.889Z Authors: Felipe Moreno-Vera, Edgar Medina, Jorge Poco Published: 2023-08-29T02:50:36.000Z ## Abstract 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. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. ## Original sources and implementation [Original paper]() [PDF]()