Posts tagged CNN

This last year I’ve been getting back into machine learning and AI, rediscovering the things that drew me to it in the first place. I’m still in the “learning” and “small studies” phase that naturally precedes crafting any new artwork, and I wanted to share some of that process here. This is a fairly linear record of my path, but my hope is that this post is modular enough that anyone interested in a specific part can skip ahead and find something that gets them excited, too. I’ll cover some experiments with these general topics: Convolutional Neural Networks, Recurrent Neural Networks, Dimensionality Reduction and Visualization, Autoencoders

via https://medium.com/@kcimc/a-return-to-machine-learning–2de3728558eb

State-of-the-art deep neural networks have achieved impressive results on many image classification tasks. However, these same architectures have been shown to be unstable to small, well sought, perturbations of the images. In this paper, we fill this gap and propose the DeepFool framework to efficiently compute perturbations that fools deep network and thus reliably quantify the robustness of arbitrary classifiers.

via http://gitxiv.com/posts/iZpWrvKdXmtRaQusa/deepfool-a-simple-and-accurate-method-to-fool-deep-neural