contrastive-semantic-projection-neuron-labeling

name	contrastive-semantic-projection-neuron-labeling
description	Contrastive Semantic Projection (CSP) for faithful neuron labeling in deep networks using contrastive examples. Two-stage pipeline with VLM-based candidate generation and CLIP-based label assignment. Improves interpretability and explanation faithfulness. Activation: neuron labeling, contrastive examples, neural network interpretability, feature visualization, semantic projection.

Contrastive Semantic Projection for Neuron Labeling

Two-stage neuron labeling framework using contrastive examples to improve faithfulness and semantic granularity: VLM-based candidate generation followed by CLIP-based label assignment.

Metadata

• Source: arXiv:2604.22477v1
• Authors: Neural Network Interpretability Research Team
• Published: 2026-04-24
• Category: Neural Network Interpretability, Feature Visualization, Explainable AI

Core Methodology

Key Innovation

Addresses limitations of existing neuron labeling approaches that rely solely on highly activating examples by incorporating contrastive examples - inputs semantically similar to activating examples but eliciting low activations - to sharpen explanations and improve label specificity.

Two-Stage Pipeline

Stage 1: Candidate Label Generation with VLMs

Problem: Existing approaches yield broad or misleading labels by focusing on dominant but incidental visual factors.

Solution: Use Vision-Language Models (VLMs) with contrastive image sets

Process:

Input: Activating examples + Contrastive examples
↓
VLM processes both sets
↓
Output: More specific, faithful candidate labels

Why it works:

• Contrastive examples highlight what the neuron does NOT respond to
• VLMs generate more discriminating descriptions
• Reduces focus on incidental visual factors

Stage 2: Label Assignment with CLIP-like Encoders

Method: Contrastive Semantic Projection (CSP)

Extension of SemanticLens:

• Incorporates contrastive examples directly into scoring pipeline
• Uses CLIP-based similarity scoring
• Improved selection criteria

Algorithm:

For each candidate label:
1. Compute CLIP similarity to activating examples
2. Compute CLIP similarity to contrastive examples
3. Calculate contrastive score: sim(activate) - sim(contrast)
4. Select label with highest contrastive score

Technical Framework

Contrastive Example Generation

Definition: Inputs that are:

• Semantically similar to activating examples
• But elicit low activations from the neuron

Generation Methods:

• FALCON-style approach: Find near neighbors with low activation
• Perturbation-based: Apply targeted modifications
• Synthetic generation: Create semantic variants

CLIP-Based Scoring

Components:

• Image encoder: Projects images to embedding space
• Text encoder: Projects candidate labels to same space
• Similarity metric: Cosine similarity between embeddings

Contrastive Scoring Function:

def contrastive_score(label, activate_imgs, contrast_imgs, clip_model):
    label_emb = clip_model.encode_text(label)
    
    # Similarity to activating examples
    activate_embs = clip_model.encode_images(activate_imgs)
    activate_sim = mean(cosine_similarity(label_emb, activate_embs))
    
    # Similarity to contrastive examples
    contrast_embs = clip_model.encode_images(contrast_imgs)
    contrast_sim = mean(cosine_similarity(label_emb, contrast_embs))
    
    # Contrastive score
    return activate_sim - contrast_sim

Implementation Guide

Prerequisites

• Vision-Language Model (e.g., CLIP, BLIP)
• Pre-trained neural network for analysis
• Dataset of images for activation analysis
• Access to neuron activation values

Step-by-Step

1. Collect Activating Examples

def collect_activating_examples(model, dataset, neuron_id, top_k=10):
    """
    Find images with highest activation for given neuron
    """
    activations = []
    for img in dataset:
        act = model.get_activation(img, neuron_id)
        activations.append((img, act))
    
    # Sort by activation
    activations.sort(key=lambda x: x[1], reverse=True)
    return [img for img, _ in activations[:top_k]]

2. Generate Contrastive Examples

def generate_contrastive_examples(model, activating_imgs, neuron_id, n=10):
    """
    Generate contrastive examples (semantic similar, low activation)
    """
    contrastive = []
    # Implementation depends on FALCON or similar approach
    # Find semantically similar images with low activation
    return contrastive

3. Generate Candidate Labels with VLM

def generate_labels_with_vlm(vlm_model, activating_imgs, contrastive_imgs):
    """
    Use VLM to generate candidate labels given both image sets
    """
    prompt = """
    These images HIGHLY activate the neuron: [activating images]
    These similar images DO NOT activate the neuron: [contrastive images]
    What concept does this neuron detect?
    """
    candidates = vlm_model.generate(prompt)
    return candidates

4. Apply Contrastive Semantic Projection

def csp_label_selection(candidate_labels, activating_imgs, 
                        contrastive_imgs, clip_model):
    """
    Select best label using contrastive semantic projection
    """
    best_label = None
    best_score = -inf
    
    for label in candidate_labels:
        score = contrastive_score(label, activating_imgs, 
                                   contrastive_imgs, clip_model)
        if score > best_score:
            best_score = score
            best_label = label
    
    return best_label, best_score

Applications

Neural Network Interpretability

• Automated neuron labeling for deep networks
• Feature visualization with semantic descriptions
• Network architecture understanding

Model Debugging

• Identifying spurious correlations
• Detecting unintended behaviors
• Validating model learning

Scientific Discovery

• Understanding visual representations
• Comparing across model architectures
• Tracking concept evolution during training

Case Study: Melanoma Detection

Application: Analyzing neurons in skin lesion classification

Findings:

• CSP labels more faithful to actual neuron function
• Improved semantic granularity over baselines
• Better identification of clinically relevant features

Pitfalls

Limitations

• Requires access to model internals (activations)
• Computationally expensive for large networks
• VLM bias may affect candidate generation
• Contrastive example quality critical

Common Issues

1. Poor contrastive examples: May not effectively discriminate
2. VLM hallucination: Generated labels may not match visual content
3. CLIP bias: Text-image alignment limitations
4. Activation noise: Unstable neuron responses

Evaluation Metrics

Faithfulness

• Alignment with human annotations
• Consistency across similar neurons
• Robustness to input perturbations

Semantic Granularity

• Specificity of labels
• Discriminative power
• Hierarchy capture

Related Skills

• llm-concept-neurons-control
• representation-steering
• brain-inspired-capture-evidence-driven

References

• arXiv:2604.22477v1 - Contrastive Semantic Projection: Faithful Neuron Labeling with Contrastive Examples
• FALCON (prior work on contrastive examples)
• SemanticLens (baseline method)