Papers with Code 아카이브

Interpretability Techniques for Deep Learning

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하위 태스크: 0
평가표: 2
보존된 평가 기록: 14

코드

관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.

https://github.com/STAC-USC/DeepNNK_polytope_interpolation

공식 코드

DeepNNK: Explaining deep models and their generalization using polytope interpolation

https://github.com/a-fsh-r/IBO

공식 코드

IBO: Inpainting-Based Occlusion to Enhance Explainable Artificial Intelligence Evaluation in Histopathology

https://github.com/alshedivat/cen

공식 코드

Contextual Explanation Networks

https://github.com/ankurtaly/Attributions

공식 코드

Axiomatic Attribution for Deep Networks

https://github.com/aryamanarora/causalgym

공식 코드

CausalGym: Benchmarking causal interpretability methods on linguistic tasks

https://github.com/ashfarhangi/covid-19

공식 코드

A Novel Deep Learning Model for Hotel Demand and Revenue Prediction amid COVID-19

https://github.com/asmadotgh/dissect

공식 코드

DISSECT: Disentangled Simultaneous Explanations via Concept Traversals

https://github.com/eclique/RISE

공식 코드

RISE: Randomized Input Sampling for Explanation of Black-box Models

https://github.com/esl-epfl/cross-domain-saliency-maps

공식 코드

Time series saliency maps: explaining models across multiple domains

https://github.com/esl-epfl/cross-domain-saliency-maps-paper

공식 코드

Time series saliency maps: explaining models across multiple domains

https://github.com/google-research/google-research/tree/master/pruning_identified_exemplars

공식 코드

What Do Compressed Deep Neural Networks Forget?

https://github.com/marcotcr/lime-experiments

공식 코드

"Why Should I Trust You?": Explaining the Predictions of Any Classifier

https://github.com/martenlienen/finite-element-networks

공식 코드

Learning the Dynamics of Physical Systems from Sparse Observations with Finite Element Networks

https://github.com/paulnovello/hsic-attribution-method

공식 코드

Making Sense of Dependence: Efficient Black-box Explanations Using Dependence Measure

https://github.com/ramprs/grad-cam

공식 코드

Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization

https://github.com/ruoyuchen10/smdl-attribution

공식 코드

Less is More: Fewer Interpretable Region via Submodular Subset Selection

https://github.com/seed-vt/tracefl

공식 코드

TraceFL: Interpretability-Driven Debugging in Federated Learning via Neuron Provenance

https://github.com/soumickmj/TorchEsegeta

공식 코드

Exploration of Interpretability Techniques for Deep COVID-19 Classification using Chest X-ray Images

https://github.com/soumickmj/diagnoPP

공식 코드

Exploration of Interpretability Techniques for Deep COVID-19 Classification using Chest X-ray Images

https://github.com/warisgill/tracefl

공식 코드

TraceFL: Interpretability-Driven Debugging in Federated Learning via Neuron Provenance

https://github.com/632652101/VisualizeCNN-Pd

Deep Inside Convolutional Networks: Visualising Image Classification Models and Saliency Maps

https://github.com/AhnYoungBin/Grad_cam

Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization

https://github.com/AlanYangYi/GayPhotoStyleRecongnizer

Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization

https://github.com/AlanYangYi/PhotoStyleRecongnizer

Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization

https://github.com/AldrichCabrera/gradCam-smallvgg

Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization

https://github.com/AlejandroAttento/Pytorch-Captum

Axiomatic Attribution for Deep Networks

https://github.com/AlexIoannides/lime-interpretable-ml

"Why Should I Trust You?": Explaining the Predictions of Any Classifier

https://github.com/AlexSzen/wwf_shark_id

Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization

https://github.com/Ankush96/grad-cam.tensorflow

Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization

https://github.com/CMU-CREATE-Lab/deep-smoke-machine

Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization

https://github.com/Cherrydomini/Effects-of-feature-dropping-on-COMPAS-with-the-influence-of-LIME-

"Why Should I Trust You?": Explaining the Predictions of Any Classifier

https://github.com/Cloud-CV/Grad-CAM

Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization

https://github.com/Deepak3994/Grad-Cam

Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization

https://github.com/Deepak3994/grad-cam-editted

Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization

https://github.com/Eljefemasao/Customed-Object-Detector

Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization

https://github.com/Eljefemasao/MarkNet-ObjectDetector

Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization

출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.