  Deep learning is increasingly used in decision-making tasks. However,
understanding how neural networks produce final predictions remains a
fundamental challenge. Existing work on interpreting neural network predictions
for images often focuses on explaining predictions for single images or
neurons. As predictions are often computed from millions of weights that are
optimized over millions of images, such explanations can easily miss a bigger
picture. We present Summit, an interactive system that scalably and
systematically summarizes and visualizes what features a deep learning model
has learned and how those features interact to make predictions. Summit
introduces two new scalable summarization techniques: (1) activation
aggregation discovers important neurons, and (2) neuron-influence aggregation
identifies relationships among such neurons. Summit combines these techniques
to create the novel attribution graph that reveals and summarizes crucial
neuron associations and substructures that contribute to a model's outcomes.
Summit scales to large data, such as the ImageNet dataset with 1.2M images, and
leverages neural network feature visualization and dataset examples to help
users distill large, complex neural network models into compact, interactive
visualizations. We present neural network exploration scenarios where Summit
helps us discover multiple surprising insights into a prevalent, large-scale
image classifier's learned representations and informs future neural network
architecture design. The Summit visualization runs in modern web browsers and
is open-sourced.
