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Visual Interpretability for Deep Learning: a Survey
v1v2 (latest)

Visual Interpretability for Deep Learning: a Survey

2 February 2018
Quanshi Zhang
Song-Chun Zhu
    FaMLHAI
ArXiv (abs)PDFHTML

Papers citing "Visual Interpretability for Deep Learning: a Survey"

8 / 258 papers shown
Title
Object-Oriented Dynamics Predictor
Object-Oriented Dynamics Predictor
Guangxiang Zhu
Zhiao Huang
Chongjie Zhang
AI4CE
89
35
0
25 May 2018
Unsupervised Learning of Neural Networks to Explain Neural Networks
Unsupervised Learning of Neural Networks to Explain Neural Networks
Quanshi Zhang
Yu Yang
Yuchen Liu
Ying Nian Wu
Song-Chun Zhu
FAttSSL
59
27
0
18 May 2018
Disentangling Controllable and Uncontrollable Factors of Variation by
  Interacting with the World
Disentangling Controllable and Uncontrollable Factors of Variation by Interacting with the World
Yoshihide Sawada
DRL
68
10
0
19 Apr 2018
Entanglement-guided architectures of machine learning by quantum tensor
  network
Entanglement-guided architectures of machine learning by quantum tensor network
Yuhan Liu
Xiao Zhang
M. Lewenstein
Shi-Ju Ran
76
32
0
24 Mar 2018
Interpreting CNNs via Decision Trees
Interpreting CNNs via Decision Trees
Quanshi Zhang
Yu Yang
Ying Nian Wu
Song-Chun Zhu
FAtt
88
323
0
01 Feb 2018
Interpretable Convolutional Neural Networks
Interpretable Convolutional Neural Networks
Quanshi Zhang
Ying Nian Wu
Song-Chun Zhu
FAtt
100
784
0
02 Oct 2017
Neural Style Transfer: A Review
Neural Style Transfer: A Review
Yongcheng Jing
Yezhou Yang
Zunlei Feng
Jingwen Ye
Yizhou Yu
Xiuming Zhang
144
740
0
11 May 2017
Deep Reinforcement Learning: An Overview
Deep Reinforcement Learning: An Overview
Yuxi Li
OffRLVLM
307
1,548
0
25 Jan 2017
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