Papers with Code 아카이브
Interpretability Techniques for Deep Learning
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
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코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/sunnynevarekar/pytorch-saliency-maps
Deep Inside Convolutional Networks: Visualising Image Classification Models and Saliency Maps
https://github.com/tanjimin/grad-cam-pytorch-light
Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization
https://github.com/taolicheng/understanding-dnn
Deep Inside Convolutional Networks: Visualising Image Classification Models and Saliency Maps
https://github.com/tarolangner/mri-biometry
Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization
https://github.com/teddykoker/image-forensics
Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization
https://github.com/thomasp85/lime
"Why Should I Trust You?": Explaining the Predictions of Any Classifier
https://github.com/thtang/CheXNet-with-localization
Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization
https://github.com/tleemann/road_evaluation
https://github.com/tomdyer10/fake_news
https://github.com/tristangomez44/metrics-saliency-maps
RISE: Randomized Input Sampling for Explanation of Black-box Models
https://github.com/tsurumeso/chainer-grad-cam
Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization
https://github.com/uhussai7/boldreams
https://github.com/ukplab/naacl2024-prompt-sensitivity
Deep Inside Convolutional Networks: Visualising Image Classification Models and Saliency Maps
https://github.com/vbbphc/GradCAM.jl
Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization
https://github.com/vlue-c/PyTorch-Explanations
RISE: Randomized Input Sampling for Explanation of Black-box Models
https://github.com/vlue-c/Visual-Explanation-Methods-PyTorch
RISE: Randomized Input Sampling for Explanation of Black-box Models
https://github.com/wawaku/grad-cam-keras
Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization
https://github.com/wickstrom/relax
RISE: Randomized Input Sampling for Explanation of Black-box Models
https://github.com/wsanchezdupont1/Grad-CAM
Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization
https://github.com/xaviercanche/Grad-CAM
Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization
https://github.com/xiaoyanLi629/ScRNA-seq-integration-by-Heterogeneous-Graph-transformer-neural-network
https://github.com/xn1997/pytorch-grad-cam
Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization
https://github.com/yaleCat/Grad-CAM-pytorch
Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization
https://github.com/yaroslav-moiseev/evidence-based-possibly-best-practices-in-classical-ML
https://github.com/yeefan1999/Explainable-Health-Prediction-with-Transfer-Learning
"Why Should I Trust You?": Explaining the Predictions of Any Classifier
https://github.com/yiskw713/ClassActivationMapping
Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization
https://github.com/yiskw713/RISE
Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization
https://github.com/yiskw713/SmoothGradCAMplusplus
Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization
https://github.com/yiskw713/scorecam
Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization
https://github.com/yoshihisa-furusawa/Activation_Maximization
Deep Inside Convolutional Networks: Visualising Image Classification Models and Saliency Maps
https://github.com/yramon/ShapCounterfactual
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.