Lecture 16 | Adversarial Examples and Adversarial Training скачать в хорошем качестве

Lecture 16 | Adversarial Examples and Adversarial Training

In Lecture 16, guest lecturer Ian Goodfellow discusses adversarial examples in deep learning. We discuss why deep networks and other machine learning models are susceptible to adversarial examples, and how adversarial examples can be used to attack machine learning systems. We discuss potential defenses against adversarial examples, and uses for adversarial examples for improving machine learning systems even without an explicit adversary. Keywords: Adversarial examples, Fooling images, fast gradient sign method, Clever Hans, adversarial defenses, adversarial examples in the physical world, adversarial training, virtual adversarial training, model-based optimization Slides: http://cs231n.stanford.edu/slides/201... -------------------------------------------------------------------------------------- Convolutional Neural Networks for Visual Recognition Instructors: Fei-Fei Li: http://vision.stanford.edu/feifeili/ Justin Johnson: http://cs.stanford.edu/people/jcjohns/ Serena Yeung: http://ai.stanford.edu/~syyeung/ Computer Vision has become ubiquitous in our society, with applications in search, image understanding, apps, mapping, medicine, drones, and self-driving cars. Core to many of these applications are visual recognition tasks such as image classification, localization and detection. Recent developments in neural network (aka “deep learning”) approaches have greatly advanced the performance of these state-of-the-art visual recognition systems. This lecture collection is a deep dive into details of the deep learning architectures with a focus on learning end-to-end models for these tasks, particularly image classification. From this lecture collection, students will learn to implement, train and debug their own neural networks and gain a detailed understanding of cutting-edge research in computer vision. Website: http://cs231n.stanford.edu/ For additional learning opportunities please visit: http://online.stanford.edu/