Many papers and theses provide high-level overviews of the proposed methods. Nowadays, in computer vision, natural language processing or similar research areas strongly driven by deep learning, these illustrations commonly include architectures of the used (convolutional) neural network. In this article, I want to provide a collection of examples using LaTeX and TikZ to produce nice figures of (convolutional) neural networks. All the discussed examples can also be found on GitHub.
While robustness against imperceptible adversarial examples is well-studied, robustness against visible adversarial perturbations such as adversarial patches is poorly understood. In this pre-print, we present a practical approach to obtain adversarial patches while actively optimizing their location within the image. On Cifar10 and GTSRB, we show that adversarial training on these location-optimized adversarial patches improves robustness significantly while not reducing accuracy.