Topic: interpretable-deep-learning Goto Github

👇 Here are 106 public repositories matching this topic...

1202kbs / understanding-nn
interpretable-deep-learning,Tensorflow tutorial for various Deep Neural Network visualization techniques
User: 1202kbs
tensorflow computer-vision tutorial sensitivity-analysis lrp deep-taylor-decomposition interpretable-deep-learning

1konny / class_selectivity_index
interpretable-deep-learning,On the importance of single directions for generalization(Morcos et al, ICLR 2018)
User: 1konny
class-selectivity-index interpretability interpretable-deep-learning interpretable-machine-learning

1konny / gradcam_plus_plus-pytorch
interpretable-deep-learning,A Simple pytorch implementation of GradCAM and GradCAM++
User: 1konny
gradcam gradcam-plus-plus cnn-visualization interpretable-deep-learning

ajsanjoaquin / shapley_valuation
interpretable-deep-learning,PyTorch reimplementation of computing Shapley values via Truncated Monte Carlo sampling from "What is your data worth? Equitable Valuation of Data" by Amirata Ghorbani and James Zou [ICML 2019]
User: ajsanjoaquin
interpretable-deep-learning pytorch pytorch-implementation interpretable-machine-learning explainable-ai data-valuation training-data training-data-curation fairness-ml fairness-ai fairness deeplearning machine-learning shapley-value game-theory

amirfeder / causalm
interpretable-deep-learning,CausaLM: Causal Model Explanation Through Counterfactual Language Models
User: amirfeder
Home Page: https://amirfeder.github.io/CausaLM/
nlp causal-inference interpretable-deep-learning

andreysharapov / xaience
interpretable-deep-learning,All about explainable AI, algorithmic fairness and more
User: andreysharapov
explainable-ai explainable-artificial-intelligence explainable-ml interpretable-machine-learning interpretable-ai interpretable-deep-learning interpretability

atulshanbhag / layerwise-relevance-propagation
interpretable-deep-learning,Implementation of Layerwise Relevance Propagation for heatmapping "deep" layers
User: atulshanbhag
deep-learning visualization interpretable-deep-learning interpretable-machine-learning

benjaminpatrickevans / xai
interpretable-deep-learning,Genetic programming method for explaining complex black-box models
User: benjaminpatrickevans
Home Page: https://dl.acm.org/citation.cfm?id=3321707.3321726
xai explainable-ai explainable-ml interpretable-deep-learning interpretable-machine-learning interpretable-ml

ducminhkhoi / interpretablecnn
interpretable-deep-learning,
User: ducminhkhoi
interpretable-deep-learning pytorch

epfml / interpret-lm-knowledge
interpretable-deep-learning,Extracting knowledge graphs from language models as a diagnostic benchmark of model performance (NeurIPS XAI 2021).
Organization: epfml
Home Page: https://arxiv.org/pdf/2111.08546.pdf
interpretable-deep-learning language-model knowledge-graph

frgfm / torch-cam
interpretable-deep-learning,Class activation maps for your PyTorch models (CAM, Grad-CAM, Grad-CAM++, Smooth Grad-CAM++, Score-CAM, SS-CAM, IS-CAM, XGrad-CAM, Layer-CAM)
User: frgfm
Home Page: https://frgfm.github.io/torch-cam/
pytorch python deep-learning cnn activation-maps gradcam-plus-plus gradcam saliency-map interpretability interpretable-deep-learning

hans66hsu / nn_interpretability
interpretable-deep-learning,Pytorch implementation of various neural network interpretability methods
User: hans66hsu
pytorch lrp cnn-visualization interpretable-deep-learning interpretable saliency-map deepdream guided-backpropagation integrated-gradients smoothgrad

hate-alert / hatexplain
interpretable-deep-learning,Can we use explanations to improve hate speech models? Our paper accepted at AAAI 2021 tries to explore that question.
Organization: hate-alert
explainability hate-speech bias offensive detection hatespeech interpretable-deep-learning bert-model lstm attention-lstm

inouye-lab / shapleyexplanationnetworks
interpretable-deep-learning,Implementation of the paper "Shapley Explanation Networks"
Organization: inouye-lab
machine-learning iclr2021 deep-learning interpretable-machine-learning interpretable-deep-learning explainable-ai

jackeywang96 / tesnet
interpretable-deep-learning,ICCV2021 paper: Interpretable Image Recognition by Constructing Transparent Embedding Space (TesNet)
User: jackeywang96
image-classification interpretable-deep-learning xai

jacobgil / pytorch-grad-cam
interpretable-deep-learning,Advanced AI Explainability for computer vision. Support for CNNs, Vision Transformers, Classification, Object detection, Segmentation, Image similarity and more.
User: jacobgil
Home Page: https://jacobgil.github.io/pytorch-gradcam-book
deep-learning pytorch grad-cam visualizations interpretability interpretable-ai interpretable-deep-learning score-cam class-activation-maps vision-transformers

jathurshan0330 / cross-modal-transformer
interpretable-deep-learning,Official repository of cross-modal transformer for interpretable automatic sleep stage classification. https://arxiv.org/abs/2208.06991
User: jathurshan0330
deep-learning sleep-stage-classification transformers interpretable-deep-learning

jialinwu17 / self_critical_vqa
interpretable-deep-learning,Code for NeurIPS 2019 paper ``Self-Critical Reasoning for Robust Visual Question Answering''
User: jialinwu17
vqa interpretable-deep-learning interpretable-ai explainable-ai visual-question-answering

jonathancrabbe / label-free-xai
interpretable-deep-learning,This repository contains the implementation of Label-Free XAI, a new framework to adapt explanation methods to unsupervised models. For more details, please read our ICML 2022 paper: 'Label-Free Explainability for Unsupervised Models'.
User: jonathancrabbe
artificial-intelligence autoencoders explainable-ai interpretability interpretable-deep-learning interpretable-machine-learning machine-learning neural-network representation-learning unsupervised-learning

js-mim / rl_singing_voice
interpretable-deep-learning,Unsupervised Representation Learning for Singing Voice Separation
User: js-mim
representation-learning unsupervised-learning interpretable-deep-learning denoising-autoencoders differential-dsp music-signal-processing source-separation

kundajelab / deeplift
interpretable-deep-learning,Public facing deeplift repo
Organization: kundajelab
deeplift sensitivity-analysis integrated-gradients guided-backpropagation saliency-map interpretable-deep-learning interpretability

kundajelab / fastism
interpretable-deep-learning,In-silico Saturation Mutagenesis implementation with 10x or more speedup for certain architectures.
Organization: kundajelab
cnn ism genomics interpretability interpretable-deep-learning

laura-rieger / deep-explanation-penalization
interpretable-deep-learning,Code for using CDEP from the paper "Interpretations are useful: penalizing explanations to align neural networks with prior knowledge" https://arxiv.org/abs/1909.13584
User: laura-rieger
interpretability neural-network machine-learning convolutional-neural-network pytorch explainability deep-learning explainable-ai artificial-intelligence ml

m-nauta / pipnet
interpretable-deep-learning,PIP-Net: Patch-based Intuitive Prototypes Network for Interpretable Image Classification (CVPR 2023)
User: m-nauta
concepts explainable-ai explainable-artificial-intelligence image-classification interpretable-machine-learning interpretable-ml prototypes prototypical-learning transparency xai

m-nauta / prototree
interpretable-deep-learning,ProtoTrees: Neural Prototype Trees for Interpretable Fine-grained Image Recognition, published at CVPR2021
User: m-nauta
pytorch computer-vision explainable-ai interpretability interpretable-machine-learning deep-neural-networks explainable-ml fine-grained-visual-categorization explainability interpretable-deep-learning

matlab-deep-learning / explore-deep-network-explainability-using-an-app
interpretable-deep-learning,This repository provides an app for exploring the predictions of an image classification network using several deep learning visualization techniques. Using the app, you can: explore network predictions with occlusion sensitivity, Grad-CAM, and gradient attribution methods, investigate misclassifications using confusion and t-SNE plots, visualize layer activations, and many more techniques to help you understand and explain your deep network’s predictions.
Organization: matlab-deep-learning
deep-learning matlab transfer-learning pretrained-models interpretable-deep-learning explainable-ai gradcam matlab-deep-learning

mims-harvard / timex
interpretable-deep-learning,Time series explainability via self-supervised model behavior consistency
Organization: mims-harvard
Home Page: https://zitniklab.hms.harvard.edu/projects/TimeX
deep-learning explainability explainable-ai interpretability interpretable-deep-learning time-series time-series-analysis transformers

minghuichen43 / awesome-trustworthy-deep-learning
interpretable-deep-learning,A curated list of trustworthy deep learning papers. Daily updating...
User: minghuichen43
adversarial-machine-learning security privacy deep-learning poisoning fairness backdoor green-ai ownership robustness

nadeemlab / cir
interpretable-deep-learning,Clinically-Interpretable Radiomics [MICCAI'22, CMPB'21]
Organization: nadeemlab
cancer-imaging-research clinical-interpretation-algorithm computational-imaging deep-learning feature-extraction interpretability interpretable-deep-learning medical-imaging medical-imaging-datasets python pytorch-implementation radiomics radiomics-feature-extraction radiomics-features

pietrobarbiero / pytorch_explain
interpretable-deep-learning,PyTorch Explain: Interpretable Deep Learning in Python.
User: pietrobarbiero
logic pytorch python machine-learning neural-network deep-learning explainability explainable-ai lens entropy sympy interpretability interpretable-ai interpretable-deep-learning interpretable-machine-learning

pkuai26 / at-cnn
interpretable-deep-learning,Project page for our paper: Interpreting Adversarially Trained Convolutional Neural Networks
Organization: pkuai26
adversarial-example interpretable-deep-learning

prashantsaikia / dynamic-shap-plots
interpretable-deep-learning,Enabling interactive plotting of the visualizations from the SHAP project.
User: prashantsaikia
machine-learning interpretability interpretable-machine-learning interpretable-deep-learning deep-learning shap data-visualization plotly interactive-visualizations visualization

priyamtejaswin / devise-keras
interpretable-deep-learning,Interpretable Image Search by Priyam Tejaswin and Akshay Chawla
User: priyamtejaswin
Home Page: http://35.227.39.159:5050/
deep-learning machine-learning interpretable-deep-learning image-search

qdata / attentivechrome
interpretable-deep-learning,NeurIPS17: [AttentiveChrome] Attend and Predict: Using Deep Attention Model to Understand Gene Regulation by Selective Attention on Chromatin
Organization: qdata
Home Page: http://deepchrome.org
deep-learning epigenetic-data deep-neural-network interpretable-deep-learning

rakhimovv / tcav
interpretable-deep-learning,Quantitative Testing with Concept Activation Vectors in PyTorch
User: rakhimovv
pytorch tcav interpretable-deep-learning xai

ryanchankh / redunet_paper
interpretable-deep-learning,Official NumPy Implementation of Deep Networks from the Principle of Rate Reduction (2021)
User: ryanchankh
deeplearning interpretable-deep-learning

scottgigante / m-phate
interpretable-deep-learning,Multislice PHATE for tensor embeddings
User: scottgigante
Home Page: https://arxiv.org/abs/1908.02831
visualization deep-learning neural-networks interpretable-deep-learning

seilna / cnn-units-in-nlp
interpretable-deep-learning,:scissors: Repository for our ICLR 2019 paper: Discovery of Natural Language Concepts in Individual Units of CNNs
User: seilna
interpretable-deep-learning nlp cnn iclr2019

shen-lab / deepaffinity
interpretable-deep-learning,Protein-compound affinity prediction through unified RNN-CNN
User: shen-lab
drug-discovery interpretable-deep-learning semi-supervised-learning attention-mechanism protein

shengcailiao / qaconv
interpretable-deep-learning,[ECCV 2020] QAConv: Interpretable and Generalizable Person Re-Identification with Query-Adaptive Convolution and Temporal Lifting, and [CVPR 2022] GS: Graph Sampling Based Deep Metric Learning
User: shengcailiao
Home Page: https://arxiv.org/abs/1904.10424
adaptive-convolution correspondence deep-metric-learning domain-generalization generalizability generalization image-matching interpretability interpretable-deep-learning metric-learning person-re-identification person-recognition person-reid person-reidentification person-retrieval person-search re-id re-identification reid temporal-models

shengcailiao / transmatcher
interpretable-deep-learning,[NeurIPS 2021] TransMatcher: Deep Image Matching Through Transformers for Generalizable Person Re-identification
User: shengcailiao
Home Page: https://arxiv.org/abs/2105.14432
correspondence deep-metric-learning domain-generalization generalizability generalization image-matching interpretability interpretable-deep-learning metric-learning person-re-identification person-recognition person-reid person-reidentification person-retrieval person-search re-id re-identification reid reidentification transformers

shinkyo0513 / towards-visually-explaining-video-understanding-networks-with-perturbation
interpretable-deep-learning,Attribution (or visual explanation) methods for understanding video classification networks. Demo codes for WACV2021 paper: Towards Visually Explaining Video Understanding Networks with Perturbation.
User: shinkyo0513
attribution-methods action-recognition video-classification interpretable-deep-learning deep-learning-visualization epic-kitchens

suinleelab / attributionpriors
interpretable-deep-learning,Tools for training explainable models using attribution priors.
Organization: suinleelab
machine-learning interpretable-deep-learning tensorflow artificial-intelligence

suinleelab / path_explain
interpretable-deep-learning,A repository for explaining feature attributions and feature interactions in deep neural networks.
Organization: suinleelab
machine-learning interpretable-deep-learning explainable-ai tensorflow pytorch

wanyu-lin / icml2021-gem
interpretable-deep-learning,Official code for the ICML 2021 paper "Generative Causal Explanations for Graph Neural Networks."
User: wanyu-lin
graph-neural-networks interpretable-deep-learning

williamcaicedo / iseeu
interpretable-deep-learning,ISeeU: Visually interpretable deep learning for mortality prediction inside the ICU
User: williamcaicedo
deep-learning intensive-care interpretable-deep-learning convolutional-neural-networks

wjnam / relative_attributing_propagation
interpretable-deep-learning,Interpreting DNNs, Relative attributing propagation
User: wjnam
interpretable-deep-learning explainable-ai

wl-zhao / diml
interpretable-deep-learning,[ICCV 2021] Towards Interpretable Deep Metric Learning with Structural Matching
User: wl-zhao
interpretable-deep-learning deep-metric-learning iccv2021 computer-vision deep-learning

yulongwang12 / visual-attribution
interpretable-deep-learning,Pytorch Implementation of recent visual attribution methods for model interpretability
User: yulongwang12
pytorch saliency interpretability excitation-backpropagation model-interpretability patternnet xai visual-explanations interpretable-deep-learning explanation

z80coder / db-nets
interpretable-deep-learning,∂B nets: learning discrete, boolean-valued functions by gradient descent
User: z80coder
boolean-logic interpretable-deep-learning machine-learning machine-learning-algorithms neural-networks tinyml