Papers with Code 아카이브
Image Attribution
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
- 하위 태스크
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- 평가표
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- 보존된 평가 기록
- 24
Image attribution algorithms aim to identify important regions that are highly relevant to model decisions.
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/mathworks/Grad-CAM-for-AlexNet-to-explain-the-reason-of-classification
Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization
https://github.com/matlab-deep-learning/Explore-Deep-Network-Explainability-Using-an-App
Deep Inside Convolutional Networks: Visualising Image Classification Models and Saliency Maps
https://github.com/mattemartini/explainable-and-trustworthy-ai-project
Deep Inside Convolutional Networks: Visualising Image Classification Models and Saliency Maps
https://github.com/matteo-rizzo/explainable-fruit-ripeness-classification
https://github.com/meliketoy/gradcam.pytorch
Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization
https://github.com/miaolan-xie/shap
https://github.com/mindspore-ai/contrib/tree/master/intern/Integrated-Gradient
https://github.com/moserle/TSJS-Grad-CAM
Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization
https://github.com/msandeep27/Pytorch-Grad-Cam
Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization
https://github.com/myurasov/RISE
RISE: Randomized Input Sampling for Explanation of Black-box Models
https://github.com/n-akram/GradCamDDD
Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization
https://github.com/nalbarr/coursera-ai4med-course3
Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization
https://github.com/nguyenhoa93/GradCAM_and_GuidedGradCAM_tf2
Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization
https://github.com/nikolettuce/DSC180B_06_NeuralBackedDecisionTrees
Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization
https://github.com/nishidayoshikatsu/chainer-gradcam-sample
Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization
https://github.com/notem/keras-alexnet
Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization
https://github.com/novice03/timm-vis
Deep Inside Convolutional Networks: Visualising Image Classification Models and Saliency Maps
https://github.com/nsaphra/acd
https://github.com/nyuvis/explanation_explorer
"Why Should I Trust You?": Explaining the Predictions of Any Classifier
https://github.com/ojimenezn/ml-interpretability
https://github.com/olivesgatech/explanatory-paradigms
Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization
https://github.com/olivesgatech/introspective-learning
Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization
https://github.com/openvinotoolkit/datumaro
RISE: Randomized Input Sampling for Explanation of Black-box Models
https://github.com/openvinotoolkit/openvino_xai
RISE: Randomized Input Sampling for Explanation of Black-box Models
https://github.com/palatos/RISE_tf
RISE: Randomized Input Sampling for Explanation of Black-box Models
https://github.com/pamflecista/Magisterka
https://github.com/pasrichashivam/Convnets_Grad-CAM_Grad-CAM-PlusPlus_Keras_tensorflow-2
Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization
https://github.com/peiwang062/Deliberative-explanation
Deep Inside Convolutional Networks: Visualising Image Classification Models and Saliency Maps
https://github.com/phanav/upsaclay-hygieia
Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization
https://github.com/poloclub/webshap
https://github.com/priyamtejaswin/devise-keras
"Why Should I Trust You?": Explaining the Predictions of Any Classifier
https://github.com/priyavrat-misra/xrays-and-gradcam
Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization
https://github.com/pwc-1/Paper-9/tree/main/4/Integrated-Gradient
https://github.com/pytorch/captum
Deep Inside Convolutional Networks: Visualising Image Classification Models and Saliency Maps
https://github.com/quantabox/literature
"Why Should I Trust You?": Explaining the Predictions of Any Classifier
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.