Code for the TCAV ML interpretability project
-
Updated
Jul 30, 2024 - Jupyter Notebook
Code for the TCAV ML interpretability project
Quantitative Testing with Concept Activation Vectors in PyTorch
CEBaB: Estimating the Causal Effects of Real-World Concepts on NLP Model Behavior
Repository of the course project of CMU 16-824 Visual Learning and Recognition
⚙📲Interpretability Beyond Feature Attribution: Quantitative Testing with Concept Activation Vectors (TCAV)
Implements TCAV (a model interpretation technique) using TensorFlow and discusses the method's strengths and weaknesses
Repository for Computer Science Master Thesis
Based on the papers "Interpretability Beyond Feature Attribution: QuantitativeTestingwithConceptActivationVectors(TCAV)" and Captum's instantiation https://captum.ai/docs/captum_insights, we developed this frontend for the Captum project based on the streamlit framework.
Inspecting ML models using heat map and TCAV.
Add a description, image, and links to the tcav topic page so that developers can more easily learn about it.
To associate your repository with the tcav topic, visit your repo's landing page and select "manage topics."