model-interpretability

Standardized Serverless ML Inference Platform on Kubernetes

An open source DevOps tool for packaging and versioning AI/ML models, datasets, code, and configuration into an OCI artifact.

pytorch实现Grad-CAM和Grad-CAM++,可以可视化任意分类网络的Class Activation Map (CAM)图,包括自定义的网络;同时也实现了目标检测faster r-cnn和retinanet两个网络的CAM图;欢迎试用、关注并反馈问题...

Pytorch Implementation of recent visual attribution methods for model interpretability

Interesting resources related to Explainable Artificial Intelligence, Interpretable Machine Learning, Interactive Machine Learning, Human in Loop and Visual Analytics.

Overview of different model interpretability libraries.

Class Activation Map (CAM) Visualizations in PyTorch.

A set of tools for leveraging pre-trained embeddings, active learning and model explainability for effecient document classification

surrogate quantitative interpretability for deepnets

Official repository for the paper "Instance-wise Causal Feature Selection for Model Interpretation" (CVPRW 2021)

Code for "Investigating and Simplifying Masking-based Saliency Methods for Model Interpretability" (https://arxiv.org/abs/2010.09750)

AI to Predict Yield in Aeroponics

Used the Functional API to built custom layers and non-sequential model types in TensorFlow, performed object detection, image segmentation, and interpretation of convolutions. Used generative deep learning including Auto Encoding, VAEs, and GANs to create new content.

Using machine learning models to predict if patients have chronic kidney disease based on a few features. The results of the models are also interpreted to make it more understandable to health practitioners.

Human Factors in Model Interpretability: Industry Practices, Challenges, and Needs

CNN Visualization using PyTorch

A python script for basic data cleaning/manipulation and modelling based on the open source House Sales Advanced Regression Techniques(Kaggle)

This repository contains the work in the AI engineer Cognizant virtual training and internship program from forage

Interpretability and Fairness in Machine Learning

Model interpretability for Explainable Artificial Intelligence