scholarly journals Adaptive Gaussian Noise Injection Regularization for Neural Networks

2020 ◽  
pp. 176-189
Author(s):  
Yinan Li ◽  
Fang Liu
Keyword(s):  
Neural Networks ◽  
Gaussian Noise ◽  
Noise Injection

Integration of statistical detector and Gaussian noise injection detector for adversarial example detection in deep neural networks

2019 ◽  
Vol 78 (14) ◽  
pp. 20409-20429 ◽  
Author(s):  
Weiqi Fan ◽  
Guangling Sun ◽  
Yuying Su ◽  
Zhi Liu ◽  
Xiaofeng Lu
Keyword(s):  
Neural Networks ◽  
Gaussian Noise ◽  
Deep Neural Networks ◽  
Noise Injection ◽  
Adversarial Example

scholarly journals On the Potential of Time Delay Neural Networks to Detect Indirect Coupling between Time Series

Entropy ◽  
2020 ◽  
Vol 22 (5) ◽  
pp. 584
Author(s):  
Riccardo Rossi ◽  
Andrea Murari ◽  
Pasquale Gaudio
Keyword(s):  
Neural Networks ◽  
Time Series ◽  
Artificial Neural Networks ◽  
Time Delay ◽  
Causal Relationship ◽  
Gaussian Noise ◽  
The Other ◽  
Causal Relationships ◽  
Indirect Coupling ◽  
Active Research

Determining the coupling between systems remains a topic of active research in the field of complex science. Identifying the proper causal influences in time series can already be very challenging in the trivariate case, particularly when the interactions are non-linear. In this paper, the coupling between three Lorenz systems is investigated with the help of specifically designed artificial neural networks, called time delay neural networks (TDNNs). TDNNs can learn from their previous inputs and are therefore well suited to extract the causal relationship between time series. The performances of the TDNNs tested have always been very positive, showing an excellent capability to identify the correct causal relationships in absence of significant noise. The first tests on the time localization of the mutual influences and the effects of Gaussian noise have also provided very encouraging results. Even if further assessments are necessary, the networks of the proposed architecture have the potential to be a good complement to the other techniques available in the market for the investigation of mutual influences between time series.

scholarly journals SAR Image Despeckling by Deep Neural Networks: from a Pre-Trained Model to an End-to-End Training Strategy

2020 ◽  
Vol 12 (16) ◽  
pp. 2636
Author(s):  
Emanuele Dalsasso ◽  
Xiangli Yang ◽  
Loïc Denis ◽  
Florence Tupin ◽  
Wen Yang
Keyword(s):  
Neural Networks ◽  
Gaussian Noise ◽  
State Of The Art ◽  
Additive White Gaussian Noise ◽  
Hybrid Approach ◽  
White Gaussian Noise ◽  
Training Data ◽  
Speckle Reduction ◽  
Sar Image ◽  
Sar Images

Speckle reduction is a longstanding topic in synthetic aperture radar (SAR) images. Many different schemes have been proposed for the restoration of intensity SAR images. Among the different possible approaches, methods based on convolutional neural networks (CNNs) have recently shown to reach state-of-the-art performance for SAR image restoration. CNN training requires good training data: many pairs of speckle-free/speckle-corrupted images. This is an issue in SAR applications, given the inherent scarcity of speckle-free images. To handle this problem, this paper analyzes different strategies one can adopt, depending on the speckle removal task one wishes to perform and the availability of multitemporal stacks of SAR data. The first strategy applies a CNN model, trained to remove additive white Gaussian noise from natural images, to a recently proposed SAR speckle removal framework: MuLoG (MUlti-channel LOgarithm with Gaussian denoising). No training on SAR images is performed, the network is readily applied to speckle reduction tasks. The second strategy considers a novel approach to construct a reliable dataset of speckle-free SAR images necessary to train a CNN model. Finally, a hybrid approach is also analyzed: the CNN used to remove additive white Gaussian noise is trained on speckle-free SAR images. The proposed methods are compared to other state-of-the-art speckle removal filters, to evaluate the quality of denoising and to discuss the pros and cons of the different strategies. Along with the paper, we make available the weights of the trained network to allow its usage by other researchers.

scholarly journals Noise injection for training artificial neural networks: A comparison with weight decay and early stopping

2009 ◽  
Vol 36 (10) ◽  
pp. 4810-4818 ◽  
Author(s):  
Richard M. Zur ◽  
Yulei Jiang ◽  
Lorenzo L. Pesce ◽  
Karen Drukker
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Early Stopping ◽  
Weight Decay ◽  
Noise Injection ◽  
Artificial Neural
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