How can I use shapley values for explaining generative model outputs

Question

Can you tell me How I can use Shapley values to explain generative model outputs?

Answer 1

Shapley values can be used to explain generative model outputs by attributing the model's output to individual input features, helping to understand the contribution of each feature to the generated result.

Here is the code snippet showing how:

In the above code, we are using the following key strategies:

• Model Setup: Define a simple generative model (e.g., MLP, VAE).
• Explainer: Use SHAP's KernelExplainer to compute Shapley values for the input latent space.
• Visualization: Use shap.summary_plot to visualize the contribution of each latent dimension to the generated output.

Hence, Shapley values help in understanding how different input features (e.g., latent dimensions) influence the generative output, providing a way to interpret the model’s decision-making process.