visual-explanations

There are 3 repositories under visual-explanations topic.

haofanwang / Score-CAM
Official implementation of Score-CAM in PyTorch
score-cam cam cnn-visualization-technique saliency cnn-visualization pytorch visual-explanations explainability class-activation-maps gradient-free heatmap scorecam grad-cam gradcam
Language:Python 431
yulongwang12 / visual-attribution
Pytorch Implementation of recent visual attribution methods for model interpretability
pytorch saliency interpretability excitation-backpropagation model-interpretability patternnet xai visual-explanations interpretable-deep-learning explanation excitation
Language:Jupyter Notebook 146
pbiecek / breakDown
Model Agnostics breakDown plots
data-science iml interpretability machine-learning visual-explanations xai
Language:R 103
fastcam
LLNL / fastcam
A toolkit for efficent computation of saliency maps for explainable AI attribution. This tool was developed at Lawrence Livermore National Laboratory.
saliency-maps gradcam pytorch-gradcam demo deep-learning explainable-ai saliency interpretability visual-explanations xai pytorch cnn-visualization
Language:Jupyter Notebook 45
ModelOriented / live
Local Interpretable (Model-agnostic) Visual Explanations - model visualization for regression problems and tabular data based on LIME method. Available on CRAN
model-visualization machine-learning interpretability lime visual-explanations xai iml
Language:R 35
alexandrosstergiou / Class_Feature_Visualization_Pyramid
[ICCVW 2019] PyTorch code for Class Visualization Pyramid for intpreting spatio-temporal class-specific activations throughout the network
pytorch saliency-tubes visual-explanations 3d-cnn layer-wise-saliency
Language:Python 22
vaynexie / CWOX
A XAI Framework to provide Contrastive Whole-output Explanation for Image Classification.
grad-cam lime saliency-map rise saliency explainable-ai xai cnn-visualization visual-explanations explainability mwp
Language:Jupyter Notebook 10
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.
adversarial-attacks deepfake-detection evaluation-framework explainable-ai visual-explanations
Language:Python 8
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.
deep-learning explainable-ai gradcam gradio-interface image-classification lime neural-network remote-sensing satellite-image-classification tensorflow transfer-learning visual-explanations splime
Language:Jupyter Notebook 5
satyamahesh84 / SIDU_XAI_CODE
Similarity Differences and Uniqueness Explainable AI method
explainable-ai explainable-artificial-intelligence classification cnn saliency visual-explanations class-activation-maps gradient-free heatmap deep-learning
Language:Python 4
CristianoPatricio / viconex-med
Code for the paper "ViConEx-Med: Visual Concept Explainability via Multi-Concept Token Transformer for Medical Image Analysis", 2025.
concept-based-models diabetic-retinopathy explainable-ai interpretability skin-lesion-classification transformers visual-explanations
Language:Python 2
dwil2444 / case-saliency
Official implementation of CASE: Contrastive Activation for Saliency Estimation; a diagnostic exploration and method for faithful, class-discriminative saliency maps.
cnn explainable-ai saliency visual-explanations cnn-visualization grad-cam saliency-map xai
Language:Python 1
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.
deep-learning explainable-ai image-classification lave shap shapley-additive-explanations visual-explanations xai
Language:Jupyter Notebook 1

visual-explanations

haofanwang / Score-CAM

yulongwang12 / visual-attribution

pbiecek / breakDown

LLNL / fastcam

ModelOriented / live

alexandrosstergiou / Class_Feature_Visualization_Pyramid

vaynexie / CWOX

IDT-ITI / XAI-Deepfakes

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

satyamahesh84 / SIDU_XAI_CODE

CristianoPatricio / viconex-med

dwil2444 / case-saliency

Purushothaman-natarajan / VALE-Explainer