jacobgil/vit-explain
Vit Explain
Vit-explain is a diagnostic framework designed to interpret the decision-making processes of vision transformer models. It functions as a toolkit for inspecting internal model states, allowing users to map visual attention and analyze how specific image features influence classification outcomes.
The project distinguishes itself by providing post-hoc model interpretation, which enables the analysis of trained neural networks without requiring architectural modifications or retraining. It employs techniques such as hook-based feature extraction to intercept internal activations during the forward pass, alongside methods like transformer attention rollout and layer-wise relevance propagation to trace the flow of information from input to output.
By generating heatmaps that track attention flow and weight distribution, the framework visualizes which specific pixels and patterns drive a model's predictions. It supports class-specific explanations by filtering attention maps with target class gradients, providing a diagnostic utility for debugging deep learning models and identifying the visual elements that contribute to classification results.
Features
- Deep Learning Interpretability - Analyzes neural network decision-making processes by mapping visual attention weights and gradients to input image regions.
- Transformer Explainability - Analyzes how vision transformer models process visual data by generating heatmaps that track attention flow across image regions.
- Attention Rollout Techniques - Aggregates attention maps across multiple layers by recursively multiplying weight matrices to trace the flow of information.
- Attention Visualizations - Generates heatmaps that track the flow of attention across image regions to reveal model processing priorities.
- Computer Vision Model Debugging - Visualizes internal decision-making processes by highlighting specific pixels and patterns that drive classification results.
- Internal Activation Hooks - Intercepts internal tensor activations during the forward pass to capture intermediate attention maps without modifying model architecture.
- Attribution Gradients - Calculates feature importance by backpropagating gradients from target classification outputs to input image patches.
- Relevance Propagation Methods - Distributes classification scores backward through network layers to identify which specific input pixels contributed most to the final prediction.
- Class-Specific Explainers - Filters attention maps with target class gradients to isolate visual elements contributing to specific classification outcomes.
- Transformer Explainability Tools - Provides diagnostic utilities for visualizing attention maps and gradients to interpret vision transformer classification features.
- Architecture Interpretation - Analyzes trained model weights and gradients to provide visual explanations without requiring retraining or architectural changes.
Star history
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