Symmetry/Asymmetry in Neural Networks and Applications

A special issue of Symmetry (ISSN 2073-8994). This special issue belongs to the section "Computer".

Deadline for manuscript submissions: 31 December 2024 | Viewed by 1576

Special Issue Editors


E-Mail Website
Guest Editor
School of Computer Science and Engineer, Ocean University of China, Qing Dao 266100, China
Interests: deep learning; network and information security; anomaly detection

E-Mail Website
Guest Editor
School of Computer Science and Engineer, Ocean University of China, Qing Dao 266100, China
Interests: perception of intelligent network streaming media; underwater image application technology; applications such as smart homes; medical big data

Special Issue Information

Dear Colleagues,

This Special Issue aims to explore the application of symmetry in the field of neural network learning, as well as the application of symmetry and asymmetry to the training of fully supervised, semi-supervised, and unsupervised models. Deep learning models such as the auto-encoder network, adversarial generative network (GAN), graph neural network (GNN), distillation learning, and twin network all exhibit strong symmetry. Applying these techniques in many industrial and agricultural fields, including object recognition, image segmentation, pedestrian re-recognition, image compression, time series prediction, and anomaly detection applications, these deep learning models have shown good performance in many application fields. This Special Issue will look at the future direction of machine learning, especially deep learning theory and practice, inspired by various symmetries.

We are therefore inviting manuscript submissions. Topics of interest include, but are not limited to, the following:

  • Target recognition;
  • Image segmentation;
  • Image compression;
  • Medical image processing;
  • Action recognition;
  • Pedestrian re-recognition;
  • Time series prediction;
  • Anomaly detection.

Dr. Peishun Liu
Prof. Dr. Ruichun Tang
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Symmetry is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • neural network
  • artificial intelligence
  • deep learning
  • adversarial learning
  • self-supervised learning
  • feature engineering

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Published Papers (1 paper)

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Research

21 pages, 6596 KiB  
Article
MRACNN: Multi-Path Residual Asymmetric Convolution and Enhanced Local Attention Mechanism for Industrial Image Compression
by Zikang Yan, Peishun Liu, Xuefang Wang, Haojie Gao, Xiaolong Ma and Xintong Hu
Symmetry 2024, 16(10), 1342; https://doi.org/10.3390/sym16101342 - 10 Oct 2024
Viewed by 965
Abstract
The rich information and complex background of industrial images make it a challenging task to improve the high compression rate of images. Current learning-based image compression methods mostly use customized convolutional neural networks (CNNs), which find it difficult to cope with the complex [...] Read more.
The rich information and complex background of industrial images make it a challenging task to improve the high compression rate of images. Current learning-based image compression methods mostly use customized convolutional neural networks (CNNs), which find it difficult to cope with the complex production background of industrial images. This causes useful information to be lost in the abundance of irrelevant data, making it difficult to accurately extract important features during the feature extraction stage. To address this, a Multi-path Residual Asymmetric Convolutional Compression Network (MRACNN) is proposed. Firstly, a Multi-path Residual Asymmetric Convolution Block (MRACB) is introduced, which includes the Multi-path Residual Asymmetric Convolution Down-sampling Module for down-sampling in the encoder to extract key features, and the Mult-path Residual Asymmetric Convolution Up-sampling Module for up-sampling in the decoder to recover details and reconstruct the image. This feature transfer and information flow enables the better capture of image details and important information, thereby improving the quality and efficiency of image compression and decompression. Furthermore, a two-branch enhanced local attention mechanisms, and a channel-squeezing entropy model based on the compression-based enhanced local attention module is proposed to enhance the performance of the modeled compression. Extensive experimental evaluations demonstrate that the proposed method outperforms state-of-the-art techniques, achieves superior Rate–Distortion Performance, and excels in preserving local details. Full article
(This article belongs to the Special Issue Symmetry/Asymmetry in Neural Networks and Applications)
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