Title: ActiVis: Visual Exploration of Industry-Scale Deep Neural Network Models,
Abstract: While deep learning models have achieved state-of-the-art accuracies for many
prediction tasks, understanding these models remains a challenge. Despite the
recent interest in developing visual tools to help users interpret deep
learning models, the complexity and wide variety of models deployed in
industry, and the large-scale datasets that they used, pose unique design
challenges that are inadequately addressed by existing work. Through
participatory design sessions with over 15 researchers and engineers at
Facebook, we have developed, deployed, and iteratively improved ActiVis, an
interactive visualization system for interpreting large-scale deep learning
models and results. By tightly integrating multiple coordinated views, such as
a computation graph overview of the model architecture, and a neuron activation
view for pattern discovery and comparison, users can explore complex deep
neural network models at both the instance- and subset-level. ActiVis has been
deployed on Facebook's machine learning platform. We present case studies with
Facebook researchers and engineers, and usage scenarios of how ActiVis may work
with different models.