Please wait, while we are loading the content...
Please wait, while we are loading the content...
Content Provider | Directory of Open Access Journals (DOAJ) |
---|---|
Author | Satoshi Suzuki Shoichiro Takeda Naoki Makishima Atsushi Ando Ryo Masumura Hayaru Shouno |
Abstract | Anti-aliased convolutional neural networks (CNNs) are models that introduce blur filters to intermediate representations of CNNs to achieve high accuracy in image recognition tasks. A promising way to prepare a new anti-aliased CNN is to introduce blur filters to the intermediate representations of pre-trained (non anti-aliased) CNNs, since many researchers have released them online. Although this scheme can build the new anti-aliased CNN easily, the blur filters drastically degrade the pre-trained representations. Therefore, to take full advantage of the benefits of introducing blur filters, fine-tuning using massive amounts of training data is often required. This can be problematic because the training data is often limited. In such a “data-limited” situation, the fine-tuning does not bring about a high performance because it induces overfitting to the limited training data. To tackle this problem, we propose “knowledge transferred fine-tuning.” Knowledge transfer is a technique that utilizes the representations of a pre-trained model to help ensure generalization in data-limited situations. Inspired by this concept, we transfer knowledge from intermediate representations in a pre-trained CNN to an anti-aliased CNN while fine-tuning. The key idea of our method is to transfer only the essential knowledge for image recognition in the pre-trained CNN using two types of loss: pixel-level loss and global-level loss. The former loss transfers the detailed knowledge from the pre-trained CNN, but this knowledge may contain “aliased” non-essential knowledge. The latter loss, on the other hand, is designed to increase when the pixel-level loss transfers non-essential knowledge while ignoring the essential knowledge, i.e., it penalizes the pixel-level loss. Experimental results demonstrate that the proposed method using just 25 training images per class on ImageNet 2012 can achieve higher accuracy than a conventional pre-trained CNN. |
e-ISSN | 21693536 |
DOI | 10.1109/ACCESS.2022.3186101 |
Journal | IEEE Access |
Volume Number | 10 |
Language | English |
Publisher | IEEE |
Publisher Date | 2022-01-01 |
Publisher Place | United States |
Access Restriction | Open |
Subject Keyword | Electrical Engineering. Electronics. Nuclear Engineering Convolutional Neural Network Data-limited Situation Knowledge Transfer Anti-aliasing |
Content Type | Text |
Resource Type | Article |
National Digital Library of India (NDLI) is a virtual repository of learning resources which is not just a repository with search/browse facilities but provides a host of services for the learner community. It is sponsored and mentored by Ministry of Education, Government of India, through its National Mission on Education through Information and Communication Technology (NMEICT). Filtered and federated searching is employed to facilitate focused searching so that learners can find the right resource with least effort and in minimum time. NDLI provides user group-specific services such as Examination Preparatory for School and College students and job aspirants. Services for Researchers and general learners are also provided. NDLI is designed to hold content of any language and provides interface support for 10 most widely used Indian languages. It is built to provide support for all academic levels including researchers and life-long learners, all disciplines, all popular forms of access devices and differently-abled learners. It is designed to enable people to learn and prepare from best practices from all over the world and to facilitate researchers to perform inter-linked exploration from multiple sources. It is developed, operated and maintained from Indian Institute of Technology Kharagpur.
Learn more about this project from here.
NDLI is a conglomeration of freely available or institutionally contributed or donated or publisher managed contents. Almost all these contents are hosted and accessed from respective sources. The responsibility for authenticity, relevance, completeness, accuracy, reliability and suitability of these contents rests with the respective organization and NDLI has no responsibility or liability for these. Every effort is made to keep the NDLI portal up and running smoothly unless there are some unavoidable technical issues.
Ministry of Education, through its National Mission on Education through Information and Communication Technology (NMEICT), has sponsored and funded the National Digital Library of India (NDLI) project.
Sl. | Authority | Responsibilities | Communication Details |
---|---|---|---|
1 | Ministry of Education (GoI), Department of Higher Education |
Sanctioning Authority | https://www.education.gov.in/ict-initiatives |
2 | Indian Institute of Technology Kharagpur | Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project | https://www.iitkgp.ac.in |
3 | National Digital Library of India Office, Indian Institute of Technology Kharagpur | The administrative and infrastructural headquarters of the project | Dr. B. Sutradhar bsutra@ndl.gov.in |
4 | Project PI / Joint PI | Principal Investigator and Joint Principal Investigators of the project |
Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon |
5 | Website/Portal (Helpdesk) | Queries regarding NDLI and its services | support@ndl.gov.in |
6 | Contents and Copyright Issues | Queries related to content curation and copyright issues | content@ndl.gov.in |
7 | National Digital Library of India Club (NDLI Club) | Queries related to NDLI Club formation, support, user awareness program, seminar/symposium, collaboration, social media, promotion, and outreach | clubsupport@ndl.gov.in |
8 | Digital Preservation Centre (DPC) | Assistance with digitizing and archiving copyright-free printed books | dpc@ndl.gov.in |
9 | IDR Setup or Support | Queries related to establishment and support of Institutional Digital Repository (IDR) and IDR workshops | idr@ndl.gov.in |
Loading...
|