Contrastive Semantic Projection: Faithful Neuron Labeling with Contrastive Examples
Neuron labeling assigns textual descriptions to internal units of deep networks. Existing approaches typically rely on highly activating examples, often yielding broad or misleading labels by focusing on dominant but incidental visual factors. Prior work such as FALCON introduced contrastive examples -- inputs that are semantically similar to activating examples but elicit low activations -- to sharpen explanations, but it primarily addresses subspace-level interpretability rather than scalable neuron-level labeling. We revisit contrastive explanations for neuron-level labeling in two stages: (1) candidate label generation with vision language models (VLMs) and (2) label assignment with C...
| Authors | Oussama Bouanani, Jim Berend, Wojciech Samek et al. |
|---|---|
| Primary category | cs.CV |
| Published | 24 April 2026 |
| Last updated at source | 29 April 2026 |
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