Code and data for the NeurIPS ATTRIB 2023 paper: Automatic Discovery of Visual Circuits
Achyuta Rajaram*, Neil Chowdhury*,
Antonio Torralba , Jacob Andreas, Sarah Schwettmann
*Equal contribution
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To date, most discoveries of network subcomponents that implement human-interpretable computations in deep vision models have involved close study of single units and large amounts of human labor. We explore scalable methods for extracting the subgraph of a vision model’s computational graph that underlies recognition of a specific visual concept. We introduce a new method for identifying these subgraphs: specifying a visual concept using a few examples, and then tracing the interdependence of neuron activations across layers, or their functional connectivity. We find that our approach extracts circuits that causally affect model output, and that editing these circuits can defend large pretrained models from adversarial attacks.