laura-rieger/deep-explanation-penalization
Code for using CDEP from the paper "Interpretations are useful: penalizing explanations to align neural networks with prior knowledge" https://arxiv.org/abs/1909.13584
This project helps machine learning engineers and researchers improve the reliability and accuracy of their neural networks. It takes a trained neural network and prior knowledge about what specific parts of the input data should or shouldn't influence predictions. By applying a penalty during training, it produces a more robust neural network that aligns better with known principles and provides more trustworthy explanations for its decisions.
128 stars. No commits in the last 6 months.
Use this if you are developing neural networks and want to prevent them from learning spurious correlations in your training data, such as irrelevant image patches in medical diagnoses or gendered words in text classification.
Not ideal if you are a business user looking for a no-code solution to interpret existing models, as this tool requires familiarity with model training and code implementation.
Stars
128
Forks
14
Language
Jupyter Notebook
License
MIT
Last pushed
Mar 22, 2021
Commits (30d)
0
Get this data via API
curl "https://pt-edge.onrender.com/api/v1/quality/ml-frameworks/laura-rieger/deep-explanation-penalization"
Open to everyone — 100 requests/day, no key needed. Get a free key for 1,000/day.
Higher-rated alternatives
obss/sahi
Framework agnostic sliced/tiled inference + interactive ui + error analysis plots
tensorflow/tcav
Code for the TCAV ML interpretability project
MAIF/shapash
🔅 Shapash: User-friendly Explainability and Interpretability to Develop Reliable and Transparent...
TeamHG-Memex/eli5
A library for debugging/inspecting machine learning classifiers and explaining their predictions
csinva/imodels
Interpretable ML package 🔍 for concise, transparent, and accurate predictive modeling...