Pattern Recognition is a mature but exciting and fast developing field, which underpins developments in cognate fields such as computer vision, image processing, text and document analysis and neural networks. It is closely akin to machine learning, and also finds applications in fast emerging areas such as biometrics, bioinformatics, multimedia data analysis and most recently data science. The journal Pattern Recognition was established some 50 years ago, as the field emerged in the early years of computer science. Over the intervening years it has expanded considerably.

The journal accepts papers making original contributions to the theory, methodology and application of pattern recognition in any area, provided that the context of the work is both clearly explained and grounded in the pattern recognition literature. Papers whos primary concern falls outside the pattern recognition domain and which report routine applications of it using existing or well known methods, should be directed elsewhere. The publication policy is to publish (1) new original articles that have been appropriately reviewed by competent scientific people, (2) reviews of developments in the field, and (3) pedagogical papers covering specific areas of interest in pattern recognition. Various special issues will be organized from time to time on current topics of interest to Pattern Recognition. Submitted papers should be single column, double spaced, no less than 20 and no more than 35 (40 for a review) pages long, with numbered pages.

Special Issue on Beneficial Noise Learning
截稿日期: 2025-06-15

Noise is an emerging and popular keyword in recent years. The noise-based models have attracted more and more attention in the pattern recognition community, including but not limited to random forest, dropout in neural networks (a kind of structural beneficial noise), generative adversarial networks, adversarial training, noisy augmentation, noisy label, positive-incentive noise, diffusion models, and flow matching models. Although most of these models don’t explicitly claim that they aim to learn noise, they actually utilize the beneficial noise implicitly. In many current studies, it is pointed out that noise can also be beneficial to large models. Therefore, noise should not be simply regarded as a harmful component any more. The positivity of noise deserves more systematic studies.

Although there are plenty of noise-related models, scientific studies of beneficial noise learning are still lacking to some extent. Most of these noise-based models just use beneficial noise in a heuristic way. This Special Issue calls for papers that study several attractive, natural, and urgent questions: (1) how a model learns the beneficial noise in a controllable manner; (2) what kind of noise will be beneficial to specific models/tasks; (3) the theoretical bound of beneficial noise.
This Special Issue seeks to cover a wide range of topics related to beneficial noise learning and analysis, including but not limited to:

    Noise-based generative models such as GAN, diffusion models, and flow matching;
    Beneficial noisy and uncertain structure in deep learning models;
    Noisy model training such as beneficial noisy labels and adversarial training;
    Noisy augmentations in diverse fields such as representation learning and signal detection;
    Positive-incentive noise;
    Beneficial noise in large models;
    Beneficial noise in data acquisition;

Guest editors:

Xuelong Li, PhD
China Telecom, Beijing, China
Email: xuelong_li@chinatelecom.cn

Hongyuan Zhang, PhD
The University of Hong Kong, HK, China
Email: hyzh98@hku.hk

Murat Sensoy, PhD
Amazon, Artificial General Intelligence (AGI) Department, London, UK
Email: msensoy@amazon.co.uk

Enze Xie, PhD
NVIDIA, Santa Clara, USA
Email: enzex@nvidia.com

Manuscript submission information:

The journal submission system (Editorial Manager®) will be open for submissions to our Special Issue from November 15, 2024. When submitting your manuscript please select the article type VSI: Beneficial Noise Learning. Both the Guide for Authors and the submission portal could be found on the Journal Homepage: Guide for authors - Pattern Recognition - ISSN 0031-3203 | ScienceDirect.com by Elsevier.

Important dates 

Submission Portal Open: November 15, 2024
Submission Deadline: June 15, 2025
Acceptance Deadline: August 15, 2025

Keywords:

Noise Learning, Beneficial Noise, Information Theory, Explainability, Generative Models, Uncertainty