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DeepVisualInsight: Time-Travelling Visualization for Spatio-Temporal
  Causality of Deep Classification Training

DeepVisualInsight: Time-Travelling Visualization for Spatio-Temporal Causality of Deep Classification Training

31 December 2021
Xiangli Yang
Yun Lin
Ruofan Liu
Zhenfeng He
Chao Wang
Jinlong Dong
Hong Mei
ArXiv (abs)PDFHTML

Papers citing "DeepVisualInsight: Time-Travelling Visualization for Spatio-Temporal Causality of Deep Classification Training"

20 / 20 papers shown
Title
DIVINE: Diverse Influential Training Points for Data Visualization and
  Model Refinement
DIVINE: Diverse Influential Training Points for Data Visualization and Model Refinement
Umang Bhatt
Isabel Chien
Muhammad Bilal Zafar
Adrian Weller
TDI
33
5
0
13 Jul 2021
A Survey on Neural Network Interpretability
A Survey on Neural Network Interpretability
Yu Zhang
Peter Tiño
A. Leonardis
K. Tang
FaMLXAI
204
684
0
28 Dec 2020
XRAI: Better Attributions Through Regions
XRAI: Better Attributions Through Regions
A. Kapishnikov
Tolga Bolukbasi
Fernanda Viégas
Michael Terry
FAttXAI
62
212
0
06 Jun 2019
Learning Loss for Active Learning
Learning Loss for Active Learning
Donggeun Yoo
In So Kweon
UQCV
85
662
0
09 May 2019
Explaining Deep Neural Networks with a Polynomial Time Algorithm for
  Shapley Values Approximation
Explaining Deep Neural Networks with a Polynomial Time Algorithm for Shapley Values Approximation
Marco Ancona
Cengiz Öztireli
Markus Gross
FAttTDI
101
227
0
26 Mar 2019
Neural Network Attributions: A Causal Perspective
Neural Network Attributions: A Causal Perspective
Aditya Chattopadhyay
Piyushi Manupriya
Anirban Sarkar
V. Balasubramanian
CML
67
146
0
06 Feb 2019
UMAP: Uniform Manifold Approximation and Projection for Dimension
  Reduction
UMAP: Uniform Manifold Approximation and Projection for Dimension Reduction
Leland McInnes
John Healy
James Melville
202
9,479
0
09 Feb 2018
Interpreting CNNs via Decision Trees
Interpreting CNNs via Decision Trees
Quanshi Zhang
Yu Yang
Ying Nian Wu
Song-Chun Zhu
FAtt
73
323
0
01 Feb 2018
Distilling a Neural Network Into a Soft Decision Tree
Distilling a Neural Network Into a Soft Decision Tree
Nicholas Frosst
Geoffrey E. Hinton
420
639
0
27 Nov 2017
Grad-CAM++: Improved Visual Explanations for Deep Convolutional Networks
Grad-CAM++: Improved Visual Explanations for Deep Convolutional Networks
Aditya Chattopadhyay
Anirban Sarkar
Prantik Howlader
V. Balasubramanian
FAtt
112
2,306
0
30 Oct 2017
Sampling Matters in Deep Embedding Learning
Sampling Matters in Deep Embedding Learning
Chaoxia Wu
R. Manmatha
Alex Smola
Philipp Krahenbuhl
117
924
0
23 Jun 2017
Understanding Black-box Predictions via Influence Functions
Understanding Black-box Predictions via Influence Functions
Pang Wei Koh
Percy Liang
TDI
219
2,910
0
14 Mar 2017
Axiomatic Attribution for Deep Networks
Axiomatic Attribution for Deep Networks
Mukund Sundararajan
Ankur Taly
Qiqi Yan
OODFAtt
193
6,024
0
04 Mar 2017
Grad-CAM: Visual Explanations from Deep Networks via Gradient-based
  Localization
Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization
Ramprasaath R. Selvaraju
Michael Cogswell
Abhishek Das
Ramakrishna Vedantam
Devi Parikh
Dhruv Batra
FAtt
325
20,110
0
07 Oct 2016
Not Just a Black Box: Learning Important Features Through Propagating
  Activation Differences
Not Just a Black Box: Learning Important Features Through Propagating Activation Differences
Avanti Shrikumar
Peyton Greenside
A. Shcherbina
A. Kundaje
FAtt
87
791
0
05 May 2016
"Why Should I Trust You?": Explaining the Predictions of Any Classifier
"Why Should I Trust You?": Explaining the Predictions of Any Classifier
Marco Tulio Ribeiro
Sameer Singh
Carlos Guestrin
FAttFaML
1.2K
17,033
0
16 Feb 2016
Visualizing Large-scale and High-dimensional Data
Visualizing Large-scale and High-dimensional Data
Jian Tang
J. Liu
Ming Zhang
Qiaozhu Mei
AI4TS
75
381
0
01 Feb 2016
Deep Residual Learning for Image Recognition
Deep Residual Learning for Image Recognition
Kaiming He
Xinming Zhang
Shaoqing Ren
Jian Sun
MedIm
2.2K
194,510
0
10 Dec 2015
DeepFool: a simple and accurate method to fool deep neural networks
DeepFool: a simple and accurate method to fool deep neural networks
Seyed-Mohsen Moosavi-Dezfooli
Alhussein Fawzi
P. Frossard
AAML
154
4,905
0
14 Nov 2015
Deep Inside Convolutional Networks: Visualising Image Classification
  Models and Saliency Maps
Deep Inside Convolutional Networks: Visualising Image Classification Models and Saliency Maps
Karen Simonyan
Andrea Vedaldi
Andrew Zisserman
FAtt
314
7,317
0
20 Dec 2013
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