CLC number: TP391
On-line Access: 2018-03-10
Received: 2017-12-02
Revision Accepted: 2018-01-25
Crosschecked: 2018-01-28
Cited: 0
Clicked: 8397
Quan-shi Zhang, Song-chun Zhu. Visual interpretability for deep learning: a survey[J]. Frontiers of Information Technology & Electronic Engineering, 2018, 19(1): 27-39.
@article{title="Visual interpretability for deep learning: a survey",
author="Quan-shi Zhang, Song-chun Zhu",
journal="Frontiers of Information Technology & Electronic Engineering",
volume="19",
number="1",
pages="27-39",
year="2018",
publisher="Zhejiang University Press & Springer",
doi="10.1631/FITEE.1700808"
}
%0 Journal Article
%T Visual interpretability for deep learning: a survey
%A Quan-shi Zhang
%A Song-chun Zhu
%J Frontiers of Information Technology & Electronic Engineering
%V 19
%N 1
%P 27-39
%@ 2095-9184
%D 2018
%I Zhejiang University Press & Springer
%DOI 10.1631/FITEE.1700808
TY - JOUR
T1 - Visual interpretability for deep learning: a survey
A1 - Quan-shi Zhang
A1 - Song-chun Zhu
J0 - Frontiers of Information Technology & Electronic Engineering
VL - 19
IS - 1
SP - 27
EP - 39
%@ 2095-9184
Y1 - 2018
PB - Zhejiang University Press & Springer
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DOI - 10.1631/FITEE.1700808
Abstract: This paper reviews recent studies in understanding neural-network representations and learning neural networks with interpretable/disentangled middle-layer representations. Although deep neural networks have exhibited superior performance in various tasks, interpretability is always Achilles’ heel of deep neural networks. At present, deep neural networks obtain high discrimination power at the cost of a low interpretability of their black-box representations. We believe that high model interpretability may help people break several bottlenecks of deep learning, e.g., learning from a few annotations, learning via human–computer communications at the semantic level, and semantically debugging network representations. We focus on convolutional neural networks (CNNs), and revisit the visualization of CNN representations, methods of diagnosing representations of pre-trained CNNs, approaches for disentangling pre-trained CNN representations, learning of CNNs with disentangled representations, and middle-to-end learning based on model interpretability. Finally, we discuss prospective trends in explainable artificial intelligence.
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