/XAI

Papers and code of Explainable AI esp. w.r.t. Image classificiation

XAI

Papers and code of Explainable AI esp. w.r.t. Image classificiation

2013 Conference Papers

Title Paper Title Source Link Code Tags
Visualization of CNN Deep Inside Convolutional Networks: Visualising Image Classification Models and Saliency Maps CVPR2013 PyTorch Visualization gradient-based saliency maps

2016 Conference Papers

Title Paper Title Source Link Code Tags
CAM Learning Deep Features for Discriminative Localization CVPR2016 PyTorch (Official) class activation mapping
LIME “Why Should I Trust You?”Explaining the Predictions of Any Classifier KDD2016 PyTorch (Official) trust a prediction

2017 Conference Papers

Title Paper Title Source Link Code Tags
Grad-CAM Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization ICCV2017, CVPR2016 (original) PyTorch Visualization gradient-based saliency maps
Network Dissection Network Dissection: Quantifying Interpretability of Deep Visual Representations CVPR2017 PyTorch (Official) Visualization

2018 Conference Papers

Title Paper Title Source Link Code Tags
TCAV Interpretability Beyond Feature Attribution:Quantitative Testing with Concept Activation Vectors (TCAV) ICML2018 Tensorflow interpretability method
Interpretable CNN Interpretable Convolutional Neural Networks CVPR2018 Tensorflow explainability by design

2019 Conference Papers

Title Paper Title Source Link Code Tags
Full-grad Full-Gradient Representation for Neural Network Visualization NeurIPS2019 PyTorch (Official) Tensorflow saliency map representation
This looks like that This Looks Like That: Deep Learning for Interpretable Image Recognition NIPS2019 PyTorch (Official) object

2020 Papers

Title Paper Title Source Link Code Tags
INN Making Sense of CNNs: Interpreting Deep Representations & Their Invariances with INNs ECCV 2020 Project Page PyTorch explainability by design
X-Grad CAM Axiom-based Grad-CAM: Towards Accurate Visualization and Explanation of CNNs PyTorch