Skip to content

Using Explainability to Inform Statistical Downscaling Based on Deep Learning Beyond Standard Validation Approaches

Notifications You must be signed in to change notification settings

jgonzalezab/XAI-metrics-North-America

Folders and files

NameName
Last commit message
Last commit date

Latest commit

 

History

8 Commits
 
 
 
 
 
 
 
 
 
 
 
 

Repository files navigation

Using Explainability to Inform Statistical Downscaling Based on Deep Learning Beyond Standard Validation Approaches

This repository contains the code to reproduce the paper Using Explainability to Inform Statistical Downscaling Based on Deep Learning Beyond Standard Validation Approaches.

Installation

To properly reproduce the environments necessary to run the experiments we rely on Docker. We provide two Dockerfiles with all the required libraries to download the data, train the models, compute the predictions (Dockerfile_SD) and compute the saliency maps (Dockerfile_XAI). Inside the corresponding generated images we can find useful libraries on which we rely to run the experiments conforming this paper.

Execution

The code in this repository is ordered following the different experiments performed in the paper:

  1. Download and preprocess data (preprocessData)
  2. Train models and compute predictions (statistical-downscaling)
  3. Compute XAI metrics (XAI)

Each of these folders contains its own README with instructions for execution

About

Using Explainability to Inform Statistical Downscaling Based on Deep Learning Beyond Standard Validation Approaches

Resources

Stars

Watchers

Forks

Packages

No packages published