Automatic Discovery of Visual Circuits

Understand

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.