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title: Layer-Wise Relevance Propagation | ||
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authors: | ||
- "[[Yoann Poupart]]" | ||
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> [!caution] WIP | ||
> | ||
> This article is a work in progress. | ||
> [!tldr] TL;DR | ||
> | ||
> LRP is a method that produces pixel relevances for a given output which doesn't to be terminal. Technically the computation happens using a single back-progation pass. | ||
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> [!example] Table of content | ||
> | ||
> - [Hot Takes](#hot-takes) | ||
> - [Short-term vs Long-term](#short-term-vs-long-term) | ||
> - [The Industry Pressure](#the-industry-pressure) | ||
> - [The Curse of AI Doomerism](#the-curse-of-ai-doomerism) | ||
## LRP Framework | ||
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### Formulations | ||
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With $R_j^{[l]}$ being the $j$-th neuron's relevance of the layer $l$, and the propagation mechanism is given by the equation $\ref{eq:aggregate}$. | ||
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$$ | ||
\begin{equation} | ||
%\label{eq:aggregate} | ||
R_{j}^{[l]}=\sum_{k}\dfrac{w_{jk}}{\sum_j w_{kj}}R_k^{[l+1]} | ||
\end{equation} | ||
$$ | ||
### Different Rules | ||
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### Technical Details | ||
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## Classification Example | ||
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### Network Decomposition | ||
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### Interpretation | ||
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> [!quote] References | ||
> | ||
> [1] [[On Pixel-Wise Explanations for Non-Linear Classifier Decisions by Layer-Wise Relevance Propagation]] | ||
> [2] [[A Rigorous Study Of The Deep Taylor Decomposition]] |
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