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Wavelet Loss Function for Auto-Encoder

Qiuyu Zhu, Hu Wang, Ruixin Zhang
2021 IEEE Access  
INDEX TERMS Auto-encoder, reconstruct, generate, wavelet, loss function.  ...  For this reason, we try to propose a new loss function, Wavelet loss function, to better generate and reconstruct images.  ...  CONCLUSION This paper proposes a new loss function applied in the field of reconstruction and generation of auto-encoder, wavelet loss function, which is born out of the wavelet transform in the traditional  ... 
doi:10.1109/access.2021.3058604 fatcat:ndd4zqpw65br5llknxnln5fyhq

Brain Tumor Detection and Classification on MR Images by a Deep Wavelet Auto-Encoder Model

Isselmou Abd El Kader, Guizhi Xu, Zhang Shuai, Sani Saminu, Imran Javaid, Isah Salim Ahmad, Souha Kamhi
2021 Diagnostics  
The contribution of the deep wavelet auto-encoder model is in the analysis of pixel pattern of MR brain image and the ability to detect and classify the tumor with high accuracy, short time, and low loss  ...  The segmented MR image slices provide two two-layer using the proposed deep wavelet auto-encoder model. We then used 200 hidden units in the first layer and 400 hidden units in the second layer.  ...  The wavelet auto-encoder model utilized the wavelet function as the activation function in a conventional state, defining different resolutions.  ... 
doi:10.3390/diagnostics11091589 pmid:34573931 pmcid:PMC8471235 fatcat:vvct36iu7zdr7pg4xwz7qs736m

Optimizing Stock Market Prediction using Long Short Term Memory

2019 VOLUME-8 ISSUE-10, AUGUST 2019, REGULAR ISSUE  
and performing auto-encoding on the data.  ...  This project proposes a machine learning model to predict stock market price based on the data set available by using LSTM model for performing prediction by de-noising the data using wavelet transform  ...  Later a compile function is used to intact these layers together and to model them to work together. Figure 4 Structure of auto encoders D.  ... 
doi:10.35940/ijitee.j9824.119119 fatcat:zm4indnba5eermexgwtmvkvzee

Deep image compression in the wavelet transform domain based on high frequency sub-band prediction

Chuxi Yang, Yan Zhao, Shigang Wang
2019 IEEE Access  
In this paper, we propose to use deep neural networks for image compression in the wavelet transform domain.  ...  The entire training process is unsupervised, and the auto-encoders and the conditional probability model are trained jointly.  ...  The loss function of the system is (27) , which combines the loss of auto-encoder and the loss of probability model to yield a joint training.  ... 
doi:10.1109/access.2019.2911403 fatcat:i6fwdnghozhzvfkd3mtcdlix3q

ECG signal de-noising based on deep learning auto encoder and discrete wavelet transform

Aqeel M.Hamad alhussainy, Ammar D. Jasim
2020 International Journal of Engineering & Technology  
In this paper, we have proposed a new method for ECG signal de-noising based on deep learning Auto encoder (DL-DAE) and wavelet transform named as (WT-DAE).  ...  Different wavelet filters and threshold functions are applied in this stage.  ...  Methodology In this paper, we have proposed a new system for ECG signal de-noising based on de-noising auto encoder with discrete wavelet transform (WT-DAE).  ... 
doi:10.14419/ijet.v9i2.30499 fatcat:mqwepj7larcslcl23kcxyzi7nu

A Novel Multi-Task Learning Model with PSAE Network for Simultaneous Estimation of Surface Quality and Tool Wear in Milling of Nickel-Based Superalloy Haynes 230

Minghui Cheng, Li Jiao, Pei Yan, Huiqing Gu, Jie Sun, Tianyang Qiu, Xibin Wang
2022 Sensors  
A novel parallel-stacked auto-encoder (PSAE) network based on stacked denoising auto-encoder (SDAE) and stacked contractive auto-encoder (SCAE) was designed as the shared layers to learn deep features  ...  Then, the time-domain features were extracted from raw cutting signals and low-frequency reconstructed wavelet packet coefficients.  ...  Standard Stacked Auto-Encoder A standard stacked auto-encoder is constructed by stacking several auto-encoders.  ... 
doi:10.3390/s22134943 pmid:35808436 pmcid:PMC9269817 fatcat:v7e3gfq6ybdubnreopmekb4gpi

Stacked Auto-Encoder Based CNC Tool Diagnosis Using Discrete Wavelet Transform Feature Extraction

Jonggeun Kim, Hansoo Lee, Jeong Woo Jeon, Jong Moon Kim, Hyeon Uk Lee, Sungshin Kim
2020 Processes  
This paper proposes stacked auto-encoder based CNC machine tool diagnosis using discrete wavelet transform feature extraction to diagnose a machine tool.  ...  In various maintenance methods for keeping the condition of machine tool, condition-based maintenance can be robust to unpredicted accidents and reduce maintenance costs.  ...  Acknowledgments: This work was supported by BK21PLUS, Creative Human Resource Development Program for IT Convergence. Conflicts of Interest: The authors declare no conflict of interest.  ... 
doi:10.3390/pr8040456 fatcat:htuhthhshbdcfjgb5umpgxkxim

Wavelet Models for Video Time-Series

Sheng Ma, Chuanyi Ji
1997 Neural Information Processing Systems  
Different from the existing methods which model the timeseries in the time domain, we model the wavelet coefficients in the wavelet domain.  ...  The strength of the wavelet model includes (1) a unified approach to model both the long-range and the short-range dependence in the video traffic simultaneously, (2) a computationally efficient method  ...  function and the buffer loss probability.  ... 
dblp:conf/nips/MaJ97 fatcat:aodrxsxn4zg65am5a5yygvqolu

PR-DAD: Phase Retrieval Using Deep Auto-Decoders [article]

Leon Gugel, Shai Dekel
2022 arXiv   pre-print
In this work we provide a novel deep learning architecture PR-DAD (Phase Retrieval Using Deep Auto- Decoders), whose components are carefully designed based on mathematical modeling of the phase retrieval  ...  (see Subsection III-D for the training loss functions).  ...  Samples from the Fashion MNIST dataset depicts the training loss function used for training the encoder-decoder architecture.  ... 
arXiv:2204.09051v1 fatcat:haz4smi2pzbw5lhybdwbl55ouq

Geological Disaster Recognition on Optical Remote Sensing Images Using Deep Learning

Ying Liu, Linzhi Wu
2016 Procedia Computer Science  
In order to capture more distinct features hidden in landslide images, a particular wavelet transformation is proposed to be used as the preprocessing method.  ...  Therefore, in this paper, we propose a deep learning based landslide recognition method for optical remote sensing images.  ...  Auto-encoder Algorithm Auto-encoder We begin by recalling the traditional auto-encoder model to build deep networks.  ... 
doi:10.1016/j.procs.2016.07.144 fatcat:moagwmlu2zf6vibk53ugo5m4ka

A novel speech emotion recognition algorithm based on wavelet kernel sparse classifier in stacked deep auto-encoder model

Pengcheng Wei, Yu Zhao
2019 Personal and Ubiquitous Computing  
, denoising auto-encoder, and sparse auto-encoder to improve the Chinese speech emotion recognition.  ...  Finally, a wavelet-kernel sparse SVM classifier is applied to classify the features.  ...  For this reason, the sparse auto-encoder is appended to the end of the de-noising auto-encoder, which can get more sparse features with less information loss without reducing the dimension.  ... 
doi:10.1007/s00779-019-01246-9 fatcat:nv6kc6maffdtnhjo2qvgtvp7oi

Defending Against Adversarial Iris Examples Using Wavelet Decomposition [article]

Sobhan Soleymani, Ali Dabouei, Jeremy Dawson, Nasser M. Nasrabadi
2019 arXiv   pre-print
for each sub-band.  ...  These defense strategies are based on wavelet domain denoising of the input examples by investigating each wavelet sub-band and removing the sub-bands that are most affected by the adversary.  ...  ACKNOWLEDGEMENT This work is based upon a work supported by the Center for Identification Technology Research and the National Science Foundation under Grant #1650474.  ... 
arXiv:1908.03176v1 fatcat:l6weicyntzbqvcd4d55crfdbyu

Fully Learnable Deep Wavelet Transform for Unsupervised Monitoring of High-Frequency Time Series [article]

Gabriel Michau and Gaetan Frusque and Olga Fink
2021 arXiv   pre-print
the wavelet, the scaling and transposed filter functions, and (3) the coefficient denoising.  ...  To achieve this objective, we propose a new activation function that performs a learnable hard-thresholding of the wavelet coefficients.  ...  The authors would like to thank Christoph Preisinger for his preliminary explorations of the proposed methodology.  ... 
arXiv:2105.00899v2 fatcat:srd3fvtrhzh3relhcriigdeh4a

A Classification Supervised Auto-Encoder Based on Predefined Evenly-Distributed Class Centroids [article]

Qiuyu Zhu, Ruixin Zhang
2020 arXiv   pre-print
In addition, a new loss function is proposed to combine the loss function of classification.  ...  Based on the basic structure of the universal autoencoder, we realized the comprehensive optimal results of encoding, decoding, classification, and good model generalization performance at the same time  ...  In order to further illustrate the effectiveness of the Wavelets loss function, we chose the CSAE model with β = 0.005 and trained on the faceScrub dataset, using MSE loss and Wavelets loss, respectively  ... 
arXiv:1902.00220v3 fatcat:6q26owjk4ven5nskicjisciqwi

Modeling Lost Information in Lossy Image Compression [article]

Yaolong Wang, Mingqing Xiao, Chang Liu, Shuxin Zheng, Tie-Yan Liu
2020 arXiv   pre-print
Lossy image compression is one of the most commonly used operators for digital images.  ...  Most recently proposed deep-learning-based image compression methods leverage the auto-encoder structure, and reach a series of promising results in this field.  ...  The informative bottleneck representation in the auto-encoder framework is well-suited for lossy compression.  ... 
arXiv:2006.11999v3 fatcat:rf5iigolivasnmype36laqgdsm
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