Special Issue "Efficient Deep Learning Models for Resource-Limited Learning"
A special issue of Journal of Imaging (ISSN 2313-433X).
Deadline for manuscript submissions: 31 August 2021.
Interests: deep learning; applied machine learning; computer vision; image processing
The past two decades of intelligent learning systems have fundamentally evolved around the advancement in deep neural networks (DNNs). DNNs have become a go-to model for various problems, from basic image understanding to complex segmentation and predictive analysis using big data (BD). For example, deep convolutional neural networks (DCNNs) are the backbones of state-of-the-art object classification, object localization, computer-aided diagnosis (CADx), robotics, and autonomous vehicles. Given a large set of labeled data, DNNs’ data representation mechanism has repeatedly proven superior to conventional human-engineered features.
Despite their adoption in a wide range of applications across all the fields of natural science and engineering, they do not scale well on resource-limited conditions, such as scarcity of data and hardware support. Specifically, in areas such as medical imaging, collecting annotated data is very costly and time-consuming since it requires experts in the domain. In some cases, due to security and privacy, gathering a large amount of information is not even feasible. Hence, beyond a successful training and testing of DNNs in a laboratory setting, most real-world deployment does not have the luxury of the high-performance computing platform. To overcome these shortcomings, there is a huge demand for research and development of optimized DNNs taking various strategies, such as network pruning, convolution kernel factorization, vector quantization (VQ), with full precision (FP32) or half-precision (FP16), and other methods of model compression, such that the models do not need a large amount of data to produce a highly generalized performance in real-time and can be implemented on resource-limited hardware platforms.
We request research articles presenting techniques (methods, tools, ideas, concepts, or even literature surveys) that will contribute to the efficient development of future deep learning models for resource-limited environments
Dr. Thangarajah Akilan
Dr. Tian Lei Wang
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 papers will be 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. Journal of Imaging 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 1600 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.
- lightweight convolutional neural network
- neural network compression
- transfer learning/domain adaptations approaches
- computer vision
- medical image processing
- object classification/segmentation/localization