scholarly journals New Perspective of Interpretability of Deep Neural Networks

2020 ◽  
Author(s):  
Masanari Kimura ◽  
Masayuki Tanaka
Keyword(s):  
Neural Networks ◽  
Deep Neural Networks ◽  
New Perspective

scholarly journals Deep neural networks for waves assisted by the Wiener–Hopf method

2020 ◽  
Vol 476 (2235) ◽  
pp. 20190846 ◽  
Author(s):  
Xun Huang
Keyword(s):  
Neural Networks ◽  
Wave Equations ◽  
Deep Neural Networks ◽  
Research Strategy ◽  
Propagation Model ◽  
Physical Constraints ◽  
Aerospace Applications ◽  
Feed Forward Network ◽  
New Perspective ◽  
Hopf Method

In this work, the classical Wiener–Hopf method is incorporated into the emerging deep neural networks for the study of certain wave problems. The essential idea is to use the first-principle-based analytical method to efficiently produce a large volume of datasets that would supervise the learning of data-hungry deep neural networks, and to further explain the working mechanisms on underneath. To demonstrate such a combinational research strategy, a deep feed-forward network is first used to approximate the forward propagation model of a duct acoustic problem, which can find important aerospace applications in aeroengine noise tests. Next, a convolutional type U-net is developed to learn spatial derivatives in wave equations, which could help to promote computational paradigm in mathematical physics and engineering applications. A couple of extensions of the U-net architecture are proposed to further impose possible physical constraints. Finally, after giving the implementation details, the performance of the neural networks are studied by comparing with analytical solutions from the Wiener–Hopf method. Overall, the Wiener–Hopf method is used here from a totally new perspective and such a combinational research strategy shall represent the key achievement of this work.

scholarly journals Relative Attributing Propagation: Interpreting the Comparative Contributions of Individual Units in Deep Neural Networks

2020 ◽  
Vol 34 (03) ◽  
pp. 2501-2508 ◽  
Author(s):  
Woo-Jeoung Nam ◽  
Shir Gur ◽  
Jaesik Choi ◽  
Lior Wolf ◽  
Seong-Whan Lee
Keyword(s):  
Neural Networks ◽  
Deep Neural Networks ◽  
Relative Influence ◽  
Evaluation Metrics ◽  
Importance Score ◽  
Relative Priority ◽  
New Perspective ◽  
Conservation Rule

As Deep Neural Networks (DNNs) have demonstrated superhuman performance in a variety of fields, there is an increasing interest in understanding the complex internal mechanisms of DNNs. In this paper, we propose Relative Attributing Propagation (RAP), which decomposes the output predictions of DNNs with a new perspective of separating the relevant (positive) and irrelevant (negative) attributions according to the relative influence between the layers. The relevance of each neuron is identified with respect to its degree of contribution, separated into positive and negative, while preserving the conservation rule. Considering the relevance assigned to neurons in terms of relative priority, RAP allows each neuron to be assigned with a bi-polar importance score concerning the output: from highly relevant to highly irrelevant. Therefore, our method makes it possible to interpret DNNs with much clearer and attentive visualizations of the separated attributions than the conventional explaining methods. To verify that the attributions propagated by RAP correctly account for each meaning, we utilize the evaluation metrics: (i) Outside-inside relevance ratio, (ii) Segmentation mIOU and (iii) Region perturbation. In all experiments and metrics, we present a sizable gap in comparison to the existing literature.

Deep neural networks trained with heavier data augmentation learn features closer to representations in hIT

2018 ◽  
Author(s):  
Alex Hernández-García ◽  
Johannes Mehrer ◽  
Nikolaus Kriegeskorte ◽  
Peter König ◽  
Tim C. Kietzmann
Keyword(s):  
Neural Networks ◽  
Deep Neural Networks ◽  
Data Augmentation

Representation of adversarial images in deep neural networks and the human brain

2018 ◽  
Author(s):  
Chi Zhang ◽  
Xiaohan Duan ◽  
Ruyuan Zhang ◽  
Li Tong
Keyword(s):  
Neural Networks ◽  
Human Brain ◽  
Deep Neural Networks
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