Deep learning doesn't take a huge amount of time or computational resources. Nor does it require highly complex code, and in some cases not even a large amount of training data. Curated best practices are now available as libraries that make it easy to plug in and write your own neural network architectures using a minimal amount of code to achieve more than 90% prediction accuracies. The two most popular deep learning libraries are: (1) pytorch created by Facebook (we will be using fastai today, which is built on top of pytorch) and (2) the keras-tensorflow framework created by Google. We will build an image classifier using the Convolutional Neural Network (CNN) model to predict if a given image is that of Drogon or Vicerion (any Game of Thrones fans here in the house?

We present an interactive system enabling users to manipulate images to explore the robustness and sensitivity of deep learning image classifiers. Using modern web technologies to run in-browser inference, users can remove image features using inpainting algorithms and obtain new classifications in real time, which allows them to ask a variety of "what if" questions by experimentally modifying images and seeing how the model reacts. Our system allows users to compare and contrast what image regions humans and machine learning models use for classification, revealing a wide range of surprising results ranging from spectacular failures (e.g., a "water bottle" image becomes a "concert" when removing a person) to impressive resilience (e.g., a "baseball player" image remains correctly classified even without a glove or base). We demonstrate our system at The 2018 Conference on Computer Vision and Pattern Recognition (CVPR) for the audience to try it live. Our system is open-sourced at https://github.com/poloclub/interactive-classification. A video demo is available at https://youtu.be/llub5GcOF6w.