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 |
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 |
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 |
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 |
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 |
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 |