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Deep learning is increasingly used in decision-making tasks. However,understanding how neural networks produce final predictions remains afundamental challenge. Existing work on interpreting neural network predictionsfor images often focuses on explaining predictions for single images orneurons. As predictions are often computed from millions of weights that areoptimized over millions of images, such explanations can easily miss a biggerpicture. We present Summit, an interactive system that scalably andsystematically summarizes and visualizes what features a deep learning modelhas learned and how those features interact to make predictions. Summitintroduces two new scalable summarization techniques: (1) activationaggregation discovers important neurons, and (2) neuron-influence aggregationidentifies relationships among such neurons. Summit combines these techniquesto create the novel attribution graph that reveals and summarizes crucialneuron associations and substructures that contribute to a model's outcomes.Summit scales to large data, such as the ImageNet dataset with 1.2M images, andleverages neural network feature visualization and dataset examples to helpusers distill large, complex neural network models into compact, interactivevisualizations. We present neural network exploration scenarios where Summithelps us discover multiple surprising insights into a prevalent, large-scaleimage classifier's learned representations and informs future neural networkarchitecture design. The Summit visualization runs in modern web browsers andis open-sourced.

Summit: Scaling Deep Learning Interpretability by Visualizing Activation and Attribution Summarizations