Multi-Modal Interpretability

Advantage

By delegating the language side to a pre-trained vision-language model, multi-modal interpretability avoids the labor-intensive step of crowdsourcing per-neuron labels and scales to open-vocabulary concept sets — any text the alignment model recognises is a candidate label. The resulting concept-level explanations compose naturally with attribution: one can mask, swap, or steer a labelled neuron / SAE feature and observe the downstream effect on predictions, producing concept-conditional heatmaps and feature-visualisation videos that describe both what a unit represents and how it is used.

Limitation

Explanations inherit the biases and blind spots of the underlying alignment model: anything CLIP cannot embed cleanly (fine-grained categories, novel domains, low-resource languages, abstract qualifiers) yields noisy or empty concept rankings. Probing-image and concept-set selection also matter — different reference distributions can label the same neuron differently, so reported descriptions should always be read together with the concept set used. Finally, a high similarity score localises a concept correlationally; whether the unit is causally responsible for the model using that concept still requires interventional follow-up.

Submethods

The category typically takes two forms:

• Concept-Set Neuron Description (CLIP-Dissect): A scalable, training-free pipeline that automatically labels neurons in any vision DNN. For each neuron $u$, one collects its activations over a probing image set, summarises them into $\mathbf{a}_u$, and selects the concept $c \in \mathcal{C}$ whose CLIP text-embedding is most similar to the visual signature of those high-activating images. The resulting per-neuron descriptions cover the entire network — convolutional and transformer-based image classifiers alike — and let researchers compare neuron meanings across architectures, layers, and probing distributions. You can find a demo for this method in ./clip-dissect. This demo shows clip_dissect: Use this skill when you need to automatically describe or interpret the functionality of individual neurons in deep neural networks (DNNs) using CLIP-based semantic analysis, perform mechanistic interpretability research on vision models, dissect convolutional or transformer-based image classifiers, identify what visual concepts activate specific neurons, or compare neuron descriptions across different probing datasets and concept sets.
• Concept Relevance Propagation (Zennit-CRP): Combines layer-wise relevance propagation (LRP) with concept-conditional masks, so that, instead of a single class-attribution heatmap, one obtains a heatmap conditioned on a chosen latent concept (a neuron, channel, or SAE feature). CRP also supports relevance-maximization-driven feature visualisation and attribution graphs that connect input regions → low-level concepts → mid-level concepts → output, exposing the concept hierarchy a PyTorch model uses to make a prediction. You can find a demo for this method in ./zennit-crp. This demo shows zennit_crp: Use this skill when working with Concept Relevance Propagation (CRP) and Relevance Maximization for explainable AI in PyTorch models, including generating concept-conditional heatmaps, feature visualizations, attribution graphs, and identifying which latent concepts neural networks use for predictions.

Compose the two when the task is "component → concept vector"

The two submethods above solve different sub-problems and compose into a single pipeline whenever the goal is to assign each model component a vector in foundation-model semantic space. Treating them as alternatives in this regime is a common routing mistake — it picks CLIP-Dissect's embedding pipeline but silently drops the cropping refinement that makes the resulting concept vector clean.

The canonical concept-vector pipeline:

1. Forward probing images through the vision model; record per-component activations.
2. For each component, pick its top-K activating images.
3. (Zennit-CRP) For each (component, image) pair, compute a concept-conditional CRP heatmap and crop the image to the high-relevance region. This strips the background noise that the foundation encoder would otherwise embed.
4. (CLIP-Dissect) Embed each cropped reference image with the multi-modal model (CLIP / SigLIP / DINOv2); average into a per-component vector $\mathbf{v}_c$; cosine-rank concepts (text queries or a concept vocabulary).

Step 4 alone is the CLIP-Dissect pipeline. Step 3 is the Zennit-CRP refinement that materially improves $\mathbf{v}_c$ quality, especially for components whose top-K images carry large irrelevant backgrounds (the default case on natural-image datasets such as ImageNet). Skipping CRP yields a faster baseline but produces measurably noisier concept vectors and degraded channel-against-text-query ranking — the cropping step is a small additional code change with a non-trivial quality gain.

Select BOTH submethods together whenever the task description matches any of the following, even if the user does not name both methods explicitly:

• "build / compute per-neuron / per-channel concept vectors $\mathbf{v}_c$"
• "embed activating reference images into CLIP / SigLIP / DINOv2 for concept labeling"
• "represent each model component as a vector in foundation-model semantic space"
• "reproduce SemanticLens-style pipelines" or any reference-image embedding workflow
• "rank neurons / channels against text queries via cosine in foundation-model space"
• "label components by averaging top-K activating image embeddings"

Select CLIP-Dissect alone when the task is pure neuron labeling without per-image cropping (e.g., very large concept-vocabulary scans where the cost of per-(component, image) CRP backward passes is prohibitive, or quick first-pass interpretability scans).

Select Zennit-CRP alone when the task is explaining a single model prediction (concept-conditional heatmaps, attribution graphs, relevance flows), with no plan to embed reference images into a foundation model.