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[CVPR 2021] Official PyTorch implementation for Transformer Interpretability Beyond Attention Visualization, a novel method to visualize classifications by Transformer based networks.

deep-learning vit bert perturbation attention-visualization bert-model explainability attention-matrix vision-transformer transformer-interpretability visualize-classifications cvpr2021

Updated May 19, 2021
Jupyter Notebook

MisaOgura / flashtorch

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Visualization toolkit for neural networks in PyTorch! Demo -->

visualization machine-learning deep-learning cnn pytorch neural-networks interpretability explainability

Updated Jun 30, 2021
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alvinwan / neural-backed-decision-trees

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Making decision trees competitive with neural networks on CIFAR10, CIFAR100, TinyImagenet200, Imagenet

pytorch neural-networks imagenet image-classification pretrained-models decision-trees cifar10 interpretability pretrained-weights cifar100 tiny-imagenet explainability neural-backed-decision-trees

Updated Jun 3, 2021
Python

microsoft / responsible-ai-widgets

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Open

Error rate in the heatmap is not multiplied by 100

nushib commented Aug 7, 2021

Version of raiwidgets:
Python 3.7.11 (default, Jul 27 2021, 09:42:29) [MSC v.1916 64 bit (AMD64)] :: Anaconda, Inc. on win32
Type "help", "copyright", "credits" or "license" for more information.

import raiwidgets

print(raiwidgets.version)
0.9.2

Steps to reproduce:

Created two cohorts from the default tree: married and not married cohorts
Switched back to the al

[Error Analysis]: The context menu in the tree map and heat map need to have the same content

Open

[Error Analysis] Support for bringing in features that are not being used by the model but useful for error analysis

haofanwang / Score-CAM

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[CVPRW 2020] Official implementation of Score-CAM in Pytorch

heatmap grad-cam pytorch cam saliency class-activation-maps cnn-visualization-technique gradcam gradient-free cnn-visualization visual-explanations explainability score-cam scorecam

Updated Aug 15, 2021
Python

explainX / explainx

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Explainable AI framework for data scientists. Explain & debug any blackbox machine learning model with a single line of code.

machine-learning scikit-learn transparency blackbox bias interpretability explainable-artificial-intelligence interpretable-ai explainable-ai explainable-ml xai interpretable-machine-learning machine-learning-interpretability explainability aws-sagemaker explainx

Updated Feb 7, 2021
Jupyter Notebook

ModelOriented / modelStudio

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📍 Interactive Studio for Explanatory Model Analysis

machine-learning predictive-modeling interactive-visualizations interpretability explainable-artificial-intelligence explainable-ai explainable-ml xai model-visualization interpretable-machine-learning iml explainability explanatory-model-analysis explainable-machine-learning

Updated Aug 15, 2021
R

hila-chefer / Transformer-MM-Explainability

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[ICCV 2021- Oral] Official PyTorch implementation for Generic Attention-model Explainability for Interpreting Bi-Modal and Encoder-Decoder Transformers, a novel method to visualize any Transformer-based network. Including examples for DETR, VQA.

visualization transformers transformer vqa clip interpretability explainable-ai explainability detr lxmert visualbert

Updated Jul 22, 2021
Jupyter Notebook

sebastian-hofstaetter / matchmaker

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Training & evaluation library for text-based neural re-ranking and dense retrieval models built with PyTorch

efficiency pytorch transformer knowledge-distillation dpr explainability neural-ir neural-ranking

Updated Jul 26, 2021
Python

carla-recourse / CARLA

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CARLA: A Python Library to Benchmark Algorithmic Recourse and Counterfactual Explanation Algorithms

python benchmarking benchmark machine-learning tensorflow pytorch artificial-intelligence counterfactual explainable-ai explainable-ml explainability tensorflow2 counterfactual-explanations counterfactuals recourse

Updated Sep 8, 2021
Python

csinva / hierarchical-dnn-interpretations

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Using / reproducing ACD from the paper "Hierarchical interpretations for neural network predictions" 🧠 (ICLR 2019)

python data-science machine-learning statistics deep-neural-networks ai deep-learning neural-network jupyter-notebook ml pytorch artificial-intelligence convolutional-neural-networks acd interpretation iclr interpretability feature-importance explainable-ai explainability

Updated Aug 25, 2021
Jupyter Notebook

baldassarreFe / graph-network-explainability

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Explainability techniques for Graph Networks, applied to a synthetic dataset and an organic chemistry task. Code for the workshop paper "Explainability Techniques for Graph Convolutional Networks" (ICML19)

bioinformatics artificial-intelligence graph-networks explainability

Updated Nov 12, 2019
Jupyter Notebook

d909b / cxplain

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Causal Explanation (CXPlain) is a method for explaining the predictions of any machine-learning model.

machine-learning deep-learning interpretability explainable-ai explainable-ml explainability

Updated Aug 22, 2020
Python

hbaniecki / adversarial-explainable-ai

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💡 Adversarial attacks on model explanations, and evaluation approaches

security evaluations attacks interpretability adversarial-machine-learning adversarial-examples adversarial-attacks model-explanation interpretable-deep-learning interpretable-ai explainable-ai explainable-ml xai interpretable-machine-learning iml explainability responsible-ai adversarial-defense adversarial-xai

Updated Sep 10, 2021

laura-rieger / deep-explanation-penalization

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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

python data-science machine-learning ai deep-learning neural-network jupyter-notebook ml pytorch artificial-intelligence convolutional-neural-network fairness interpretability cdep feature-importance recurrent-neural-network interpretable-deep-learning explainable-ai explainability fairness-ml

Updated Mar 22, 2021
Jupyter Notebook

iancovert / sage

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For calculating global feature importance using Shapley values.

machine-learning interpretability shapley explainability

Updated Jul 19, 2021
Python

SAP / contextual-ai

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Contextual AI adds explainability to different stages of machine learning pipelines - data, training, and inference - thereby addressing the trust gap between such ML systems and their users. It does not refer to a specific algorithm or ML method — instead, it takes a human-centric view and approach to AI.

machine-learning report-generator explainability

Updated Sep 15, 2021
Jupyter Notebook

awslabs / sagemaker-explaining-credit-decisions

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Amazon SageMaker Solution for explaining credit decisions.

machinelearning financial-analysis credit-scoring explainable-ai explainable-ml sagemaker loan-prediction-analysis shapley explainability aws-sagemaker

Updated Jun 2, 2021
Python

hate-alert / HateXplain

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Can we use explanations to improve hate speech models? Our paper accepted at AAAI 2021 tries to explore that question.

detection lstm offensive bias hatespeech hate-speech interpretable-deep-learning attention-lstm bert-model explainability bert-fine-tuning

Updated Aug 25, 2021
Python

WHUIR / CARP

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The implementation of “A Capsule Network for Recommendation and Explaining What You Like and Dislike”, Chenliang Li, Cong Quan, Li Peng, Yunwei Qi, Yuming Deng, Libing Wu, https://dl.acm.org/citation.cfm?doid=3331184.3331216

recommendation explainability caspule-network

Updated Feb 1, 2020
Python

fat-forensics / fat-forensics

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Modular Python Toolbox for Fairness, Accountability and Transparency Forensics

machine-learning transparency fairness accountability interpretability interpretable-ai explainable-ai explainability

Updated Jan 25, 2021
Python

copenlu / ALPS_2021

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XAI Tutorial for the Explainable AI track in the ALPS winter school 2021

pytorch colab exercises interpretability colab-notebook explainability captum

Updated Feb 12, 2021
Jupyter Notebook

DFKI-NLP / thermostat

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Collection of NLP model explanations and accompanying analysis tools

natural-language-processing transformers datasets heatmaps interpretability explainability saliency-maps feature-attribution captum explainable-nlp

Updated Sep 2, 2021
Python

iancovert / removal-explanations

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A lightweight implementation of removal-based explanations for ML models.

machine-learning interpretability shapley explainability

Updated Jul 19, 2021
Python

explainability

Here are 117 public repositories matching this topic...

slundberg / shap

EthicalML / awesome-production-machine-learning

interpretml / interpret

Interpret

MAIF / shapash

h1st-ai / h1st

EthicalML / xai

hila-chefer / Transformer-Explainability

MisaOgura / flashtorch

alvinwan / neural-backed-decision-trees

microsoft / responsible-ai-widgets

Error rate in the heatmap is not multiplied by 100

[Error Analysis]: The context menu in the tree map and heat map need to have the same content

[Error Analysis] Support for bringing in features that are not being used by the model but useful for error analysis

haofanwang / Score-CAM

explainX / explainx

ModelOriented / modelStudio

hila-chefer / Transformer-MM-Explainability

sebastian-hofstaetter / matchmaker

carla-recourse / CARLA

csinva / hierarchical-dnn-interpretations

baldassarreFe / graph-network-explainability

d909b / cxplain

hbaniecki / adversarial-explainable-ai

laura-rieger / deep-explanation-penalization

iancovert / sage

SAP / contextual-ai

awslabs / sagemaker-explaining-credit-decisions

hate-alert / HateXplain

WHUIR / CARP

fat-forensics / fat-forensics

copenlu / ALPS_2021

DFKI-NLP / thermostat

iancovert / removal-explanations

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