What is Missing? Explaining Neurons Activated by Absent Concepts
Explainable artificial intelligence (XAI) aims to provide human-interpretable insights into the behavior of deep neural networks (DNNs), typically by estimating a simplified causal structure of the model. In existing work, this causal structure often includes relationships where the presence of a concept is associated with a strong activation of a neuron. For example, attribution methods primarily identify input pixels that contribute most to a prediction, and feature visualization methods reveal inputs that cause high activation of a target neuron - the former implicitly assuming that the relevant information resides in the input, and the latter that neurons encode the presence of concep...
| Authors | Robin Hesse, Simone Schaub-Meyer, Janina Hesse et al. |
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| Primary category | cs.CV |
| Published | 10 March 2026 |
| Last updated at source | 29 May 2026 |
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