Interpretable and Fine-Grained Visual Explanations for Convolutional Neural Networks | Paper Citation Record

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This paper cites Local explanation methods for deep neural networks lack sensitivity to parameter values.

Interpretable and Fine-Grained Visual Explanations for Convolutional Neural Networks Local explanation methods for deep neural networks lack sensitivity to parameter values

Reference 1

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This paper cites A survey on automated microaneurysm detection in dia- betic retinopathy retinal images.

Interpretable and Fine-Grained Visual Explanations for Convolutional Neural Networks A survey on automated microaneurysm detection in dia- betic retinopathy retinal images

Reference 2

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This paper cites Arunkumar and P.

Interpretable and Fine-Grained Visual Explanations for Convolutional Neural Networks Arunkumar and P

Reference 3

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This paper cites On pixel-wise explanations for non-linear classi- ﬁer decisions by layer-wise relevance propagation.PloS one, 10(7):e0130140, 2015.

Interpretable and Fine-Grained Visual Explanations for Convolutional Neural Networks On pixel-wise explanations for non-linear classi- ﬁer decisions by layer-wise relevance propagation.PloS one, 10(7):e0130140, 2015

Reference 4

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This paper cites How to explain individual classiﬁcation decisions.

Interpretable and Fine-Grained Visual Explanations for Convolutional Neural Networks How to explain individual classiﬁcation decisions

Reference 5

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This paper cites Explaining Image Classifiers by Counterfactual Generation.

Interpretable and Fine-Grained Visual Explanations for Convolutional Neural Networks Explaining Image Classifiers by Counterfactual Generation

Reference 6

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This paper cites Balasubramanian.

Interpretable and Fine-Grained Visual Explanations for Convolutional Neural Networks Balasubramanian

Reference 7

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This paper cites Colas, A.

Interpretable and Fine-Grained Visual Explanations for Convolutional Neural Networks Colas, A

Reference 8

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This paper cites The cityscapes dataset for semantic urban scene understanding.

Interpretable and Fine-Grained Visual Explanations for Convolutional Neural Networks The cityscapes dataset for semantic urban scene understanding

Reference 9

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This paper cites EyePACS: an adapt- able telemedicine system for diabetic retinopathy screening.

Interpretable and Fine-Grained Visual Explanations for Convolutional Neural Networks EyePACS: an adapt- able telemedicine system for diabetic retinopathy screening

Reference 10

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This paper cites Real time image saliency for black box classiﬁers.

Interpretable and Fine-Grained Visual Explanations for Convolutional Neural Networks Real time image saliency for black box classiﬁers

Reference 11

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This paper cites Doll ´ar, C.

Interpretable and Fine-Grained Visual Explanations for Convolutional Neural Networks Doll ´ar, C

Reference 12

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This paper cites Techniques for Interpretable Machine Learning.

Interpretable and Fine-Grained Visual Explanations for Convolutional Neural Networks Techniques for Interpretable Machine Learning

Reference 13

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This paper cites To- wards explanation of dnn-based prediction with guided fea- ture inversion.

Interpretable and Fine-Grained Visual Explanations for Convolutional Neural Networks To- wards explanation of dnn-based prediction with guided fea- ture inversion

Reference 14

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This paper cites https://www.kaggle.com/c/diabetic-retinopathy- detection.

Interpretable and Fine-Grained Visual Explanations for Convolutional Neural Networks https://www.kaggle.com/c/diabetic-retinopathy- detection

Reference 15

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This paper cites https://www.kaggle.com/c/diabetic-retinopathy- detection/discussion/15617.

Interpretable and Fine-Grained Visual Explanations for Convolutional Neural Networks https://www.kaggle.com/c/diabetic-retinopathy- detection/discussion/15617

Reference 16

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This paper cites Fong and Andrea Vedaldi.

Interpretable and Fine-Grained Visual Explanations for Convolutional Neural Networks Fong and Andrea Vedaldi

Reference 17

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This paper cites Gondal, Jan M.

Interpretable and Fine-Grained Visual Explanations for Convolutional Neural Networks Gondal, Jan M

Reference 18

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This paper cites Explaining and harnessing adversarial examples.

Interpretable and Fine-Grained Visual Explanations for Convolutional Neural Networks Explaining and harnessing adversarial examples

Reference 19

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This paper cites Development and validation of a deep learning algo- rithm for detection of diabetic retinopathy in retinal fundus photographs.

Interpretable and Fine-Grained Visual Explanations for Convolutional Neural Networks Development and validation of a deep learning algo- rithm for detection of diabetic retinopathy in retinal fundus photographs

Reference 20

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This paper cites A Gaussian Scale Space Approach For Exudates Detection, Classification And Severity Prediction.

Interpretable and Fine-Grained Visual Explanations for Convolutional Neural Networks A Gaussian Scale Space Approach For Exudates Detection, Classification And Severity Prediction

Reference 21

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This paper cites Learning both weights and connections for efﬁcient neural network.

Interpretable and Fine-Grained Visual Explanations for Convolutional Neural Networks Learning both weights and connections for efﬁcient neural network

Reference 22

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This paper cites Deep residual learning for image recognition.

Interpretable and Fine-Grained Visual Explanations for Convolutional Neural Networks Deep residual learning for image recognition

Reference 23

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This paper cites Distilling the Knowledge in a Neural Network.

Interpretable and Fine-Grained Visual Explanations for Convolutional Neural Networks Distilling the Knowledge in a Neural Network

Reference 24

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This paper cites The DIARETDB1 diabetic retinopathy database and evaluation protocol.

Interpretable and Fine-Grained Visual Explanations for Convolutional Neural Networks The DIARETDB1 diabetic retinopathy database and evaluation protocol

Reference 25

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This paper cites ImageNet classiﬁcation with deep convolutional neural net- works.

Interpretable and Fine-Grained Visual Explanations for Convolutional Neural Networks ImageNet classiﬁcation with deep convolutional neural net- works

Reference 26

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This paper cites Adversarial examples in the physical world.

Interpretable and Fine-Grained Visual Explanations for Convolutional Neural Networks Adversarial examples in the physical world

Reference 27

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This paper cites A location-to-segmentation strategy for automatic exudate segmentation in colour retinal fundus images.

Interpretable and Fine-Grained Visual Explanations for Convolutional Neural Networks A location-to-segmentation strategy for automatic exudate segmentation in colour retinal fundus images

Reference 28

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This paper cites De- tection of red lesions in diabetic retinopathy affected fundus images.

Interpretable and Fine-Grained Visual Explanations for Convolutional Neural Networks De- tection of red lesions in diabetic retinopathy affected fundus images

Reference 29

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This paper cites Concrete problems for autonomous vehicle safety: Advantages of Bayesian deep learning.

Interpretable and Fine-Grained Visual Explanations for Convolutional Neural Networks Concrete problems for autonomous vehicle safety: Advantages of Bayesian deep learning

Reference 30

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This paper cites A Theoretical Explanation for Perplexing Behaviors of Backpropagation-based Visualizations.

Interpretable and Fine-Grained Visual Explanations for Convolutional Neural Networks A Theoretical Explanation for Perplexing Behaviors of Backpropagation-based Visualizations

Reference 31

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This paper cites Rise: Random- ized input sampling for explanation of black-box models.

Interpretable and Fine-Grained Visual Explanations for Convolutional Neural Networks Rise: Random- ized input sampling for explanation of black-box models

Reference 32

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This paper cites Why should I trust you?: Explaining the predictions of any classiﬁer.

Interpretable and Fine-Grained Visual Explanations for Convolutional Neural Networks Why should I trust you?: Explaining the predictions of any classiﬁer

Reference 33

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This paper cites Berg, and Li Fei-Fei.

Interpretable and Fine-Grained Visual Explanations for Convolutional Neural Networks Berg, and Li Fei-Fei

Reference 34

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Interpretable and Fine-Grained Visual Explanations for Convolutional Neural Networks Selvaraju, Michael Cogswell, Abhishek Das, Ramakrishna Vedantam, Devi Parikh, and Dhruv Ba- tra

Reference 35

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This paper cites Regional Multi-scale Approach for Visually Pleasing Explanations of Deep Neural Networks.

Interpretable and Fine-Grained Visual Explanations for Convolutional Neural Networks Regional Multi-scale Approach for Visually Pleasing Explanations of Deep Neural Networks

Reference 36

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This paper cites Learning important features through propagating activation differences.

Interpretable and Fine-Grained Visual Explanations for Convolutional Neural Networks Learning important features through propagating activation differences

Reference 37

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This paper cites Deep inside convolutional networks: Visualising image clas- siﬁcation models and saliency maps.

Interpretable and Fine-Grained Visual Explanations for Convolutional Neural Networks Deep inside convolutional networks: Visualising image clas- siﬁcation models and saliency maps

Reference 38

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This paper cites Very Deep Convolutional Networks for Large-Scale Image Recognition.

Interpretable and Fine-Grained Visual Explanations for Convolutional Neural Networks Very Deep Convolutional Networks for Large-Scale Image Recognition

Reference 39

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Interpretable and Fine-Grained Visual Explanations for Convolutional Neural Networks SmoothGrad: removing noise by adding noise

Reference 40

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This paper cites Solomon, Emily Chew, Elia J.

Interpretable and Fine-Grained Visual Explanations for Convolutional Neural Networks Solomon, Emily Chew, Elia J

Reference 41

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This paper cites Striving for simplicity: The all convolutional net.

Interpretable and Fine-Grained Visual Explanations for Convolutional Neural Networks Striving for simplicity: The all convolutional net

Reference 42

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This paper cites Axiomatic attribution for deep networks.

Interpretable and Fine-Grained Visual Explanations for Convolutional Neural Networks Axiomatic attribution for deep networks

Reference 43

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This paper cites Going deeper with convolutions.

Interpretable and Fine-Grained Visual Explanations for Convolutional Neural Networks Going deeper with convolutions

Reference 44

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This paper cites In- triguing properties of neural networks.

Interpretable and Fine-Grained Visual Explanations for Convolutional Neural Networks In- triguing properties of neural networks

Reference 45

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This paper cites Development and validation of a deep learn- ing system for diabetic retinopathy and related eye diseases using retinal images from multiethnic populations with dia- betes.

Interpretable and Fine-Grained Visual Explanations for Convolutional Neural Networks Development and validation of a deep learn- ing system for diabetic retinopathy and related eye diseases using retinal images from multiethnic populations with dia- betes

Reference 46

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This paper cites Rogers, Ryo Kawasaki, Ecosse L.

Interpretable and Fine-Grained Visual Explanations for Convolutional Neural Networks Rogers, Ryo Kawasaki, Ecosse L

Reference 47

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This paper cites Zeiler and Rob Fergus.

Interpretable and Fine-Grained Visual Explanations for Convolutional Neural Networks Zeiler and Rob Fergus

Reference 48

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Observation 5992d10f-97ee-44c6-9b3c-d02b13fcb1b3 · outbound

This paper cites Top-down neural attention by excitation backprop.

Interpretable and Fine-Grained Visual Explanations for Convolutional Neural Networks Top-down neural attention by excitation backprop

Reference 49

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This paper cites Visual interpretability for deep learning: A survey.

Interpretable and Fine-Grained Visual Explanations for Convolutional Neural Networks Visual interpretability for deep learning: A survey

Reference 50

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This paper cites Uniqueness-driven saliency analysis for automated lesion detection with applications to retinal diseases.

Interpretable and Fine-Grained Visual Explanations for Convolutional Neural Networks Uniqueness-driven saliency analysis for automated lesion detection with applications to retinal diseases

Reference 51

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Observation eeba07b5-a613-4444-a54d-f839ff65d6bf · outbound

This paper cites Object Detectors Emerge in Deep Scene CNNs.

Interpretable and Fine-Grained Visual Explanations for Convolutional Neural Networks Object Detectors Emerge in Deep Scene CNNs

Reference 52

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This paper cites Learning deep features for discrimi- native localization.

Interpretable and Fine-Grained Visual Explanations for Convolutional Neural Networks Learning deep features for discrimi- native localization

Reference 53

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This paper cites Au- tomatic hemorrhage detection in color fundus images based on gradual removal of vascular branches.

Interpretable and Fine-Grained Visual Explanations for Convolutional Neural Networks Au- tomatic hemorrhage detection in color fundus images based on gradual removal of vascular branches

Reference 54

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