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hans66hsu / nn_interpretability

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Pytorch implementation of various neural network interpretability methods

pytorch deepdream saliency-map occlusion-sensitivity smoothgrad guided-backpropagation interpretable-deep-learning lrp gradient-visualization interpretable gradcam deconvnet cnn-visualization deeplift integrated-gradients activation-maximization interpretability-methods uncertainty-interpretability taylor-decomposition nn-interpretability
  • Updated Mar 8, 2022
  • Jupyter Notebook
CNN-Predictor-for-Malaria_Cells-LIME-CAM

mdhabibi / CNN-Predictor-for-Malaria_Cells-LIME-CAM

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Enhanced CNN model for malaria cell classification, featuring Class Activation Mapping (CAM) as a non-agnstic technique for anomaly localization and LIME (Local Interpretable-agnostic Explanation) for interpretability, ensuring high accuracy and transparent AI diagnostics.

deep-learning agnostic convolutional-neural-networks tuning-parameters cv2 binary-classification lime keras-tensorflow class-activation-maps explainable-ai interpretable-machine-learning deeplift non-agnostic
  • Updated Feb 15, 2024
  • Jupyter Notebook

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