visual-explanations

There are 11 repositories under visual-explanations topic.

  • haofanwang/Score-CAM

    Official implementation of Score-CAM in PyTorch

    Language:Python41062966

  • yulongwang12/visual-attribution

    Pytorch Implementation of recent visual attribution methods for model interpretability

    Language:Jupyter Notebook1454626

  • pbiecek/breakDown

    Model Agnostics breakDown plots

    Language:R103112616
  • fastcam

    LLNL/fastcam

    A toolkit for efficent computation of saliency maps for explainable AI attribution. This tool was developed at Lawrence Livermore National Laboratory.

    Language:Jupyter Notebook44726

  • ModelOriented/live

    Local Interpretable (Model-agnostic) Visual Explanations - model visualization for regression problems and tabular data based on LIME method. Available on CRAN

    Language:R359615

  • alexandrosstergiou/Class_Feature_Visualization_Pyramid

    [ICCVW 2019] PyTorch code for Class Visualization Pyramid for intpreting spatio-temporal class-specific activations throughout the network

    Language:Python22502

  • vaynexie/CWOX

    A XAI Framework to provide Contrastive Whole-output Explanation for Image Classification.

    Language:Jupyter Notebook8301

  • satyamahesh84/SIDU_XAI_CODE

    Similarity Differences and Uniqueness Explainable AI method

    Language:Python4102

  • IDT-ITI/XAI-Deepfakes

    Code, model and data for our paper: K. Tsigos, E. Apostolidis, S. Baxevanakis, S. Papadopoulos, V. Mezaris, "Towards Quantitative Evaluation of Explainable AI Methods for Deepfake Detection", Proc. ACM Int. Workshop on Multimedia AI against Disinformation (MAD’24) at the ACM Int. Conf. on Multimedia Retrieval (ICMR’24), Thailand, June 2024.

    Language:Python3202

  • Purushothaman-natarajan/eXplainable-AI-for-Image-Classification-on-Remote-Sensing

    This repository provides the training codes to classify aerial images using a custom-built model (transfer learning with InceptionResNetV2 as the backbone) and explainers to explain the predictions with LIME and GradCAM on an interface that lets you upload or paste images for classification and see visual explanations.

    Language:Jupyter Notebook2200

  • Purushothaman-natarajan/VALE-Explainer

    Language-Aware Visual Explanations (LAVE) is a framework designed for image classification tasks, particularly focusing on the ImageNet dataset. Unlike conventional methods that necessitate extensive training, LAVE leverages SHAP (SHapley Additive exPlanations) values to provide insightful textual and visual explanations.

    Language:Jupyter Notebook1