Asymmetric and Symmetric in Deep Computer Vision and Generative Modeling

Dear Colleagues,

In recent years, deep learning has transformed the fields of computer vision and generative modeling, enabling remarkable advancements across a wide range of applications, from image recognition and natural scene interpretation to the generation of realistic synthetic data. Symmetry and asymmetry are central to these fields, influencing the efficiency of model architectures, optimization techniques, and adaptability to varied data conditions. Symmetric techniques often support generalization and model stability, while asymmetric approaches provide flexible solutions to complex challenges, such as data variability and the handling of noise and anomalies.

This Special Issue seeks to showcase pioneering research that delves into the applications, theoretical insights, and methodological advancements of symmetric and asymmetric techniques within deep computer vision and generative modeling. We invite contributions that expand upon these frameworks to enhance model generalization, efficiency, and interpretability, spanning fundamental theory to practical applications. This collection of articles aims to provide a comprehensive understanding of the impact of these approaches, fostering further development in these rapidly evolving fields. Topics of interest include, but are not limited to, the following:

• Symmetric and asymmetric neural architectures in computer vision and generative tasks;
• Deep learning methods for pattern recognition, segmentation, and image generation;
• Generative models for image synthesis, inpainting, and super-resolution;
• Symmetry in feature extraction, data augmentation, and transfer learning;
• Asymmetric model training, loss functions, and optimization for vision applications;
• Cross-domain generative modeling and data augmentation;
• Use of symmetry in reducing model bias and enhancing interpretability;
• Challenges and solutions in asymmetric and symmetric data fusion;
• Generative adversarial networks (GANs), autoencoders, and transformers for image synthesis;
• Multimodal learning analytics;
• Symmetry in handling data variability and augmenting model robustness;
• Applications in fields like medical imaging, autonomous driving, and environmental monitoring;

We encourage researchers to contribute their original research articles, comprehensive reviews, or technical perspectives that explore the pivotal role of symmetry and asymmetry, driving forward the capabilities of deep computer vision and generative modeling. This Special Issue seeks to inspire new ideas, foster collaboration, and contribute to shaping the future of these dynamic research areas.

Prof. Dr. Youssef Es-Saady
Prof. Dr. Mohamed El Hajji
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.

• deep computer vision
• generative models and GANs
• symmetry and asymmetry in neural networks
• feature extraction and representation learning
• synthetic data generation
• cross-domain applications
• data augmentation and transfer learning
• model robustness and interpretability