Fast and Efficient Image Generation Using Variational Autoencoders and K-Nearest Neighbor OveRsampling Approach
<p dir="ltr">Researchers gravitate towards Generative Adversarial Networks (GAN) to create artificial images. However, GANs suffer from convergence issues, mode collapse, and overall complexity in balancing the Nash Equilibrium. Images generated are often distorted, rendering them us...
Saved in:
| Main Author: | |
|---|---|
| Other Authors: | |
| Published: |
2023
|
| Subjects: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1864513527572594688 |
|---|---|
| author | Ashhadul Islam (16869981) |
| author2 | Samir Brahim Belhaouari (9427347) |
| author2_role | author |
| author_facet | Ashhadul Islam (16869981) Samir Brahim Belhaouari (9427347) |
| author_role | author |
| dc.creator.none.fl_str_mv | Ashhadul Islam (16869981) Samir Brahim Belhaouari (9427347) |
| dc.date.none.fl_str_mv | 2023-03-20T03:00:00Z |
| dc.identifier.none.fl_str_mv | 10.1109/access.2023.3259236 |
| dc.relation.none.fl_str_mv | https://figshare.com/articles/journal_contribution/Fast_and_Efficient_Image_Generation_Using_Variational_Autoencoders_and_K-Nearest_Neighbor_OveRsampling_Approach/25204208 |
| dc.rights.none.fl_str_mv | CC BY 4.0 info:eu-repo/semantics/openAccess |
| dc.subject.none.fl_str_mv | Engineering Electrical engineering Electronics, sensors and digital hardware Materials engineering Generators Feature extraction Generative adversarial networks Decoding Training Nash equilibrium Image synthesis Face recognition variational autoencoders Image reconstruction artificial image creation |
| dc.title.none.fl_str_mv | Fast and Efficient Image Generation Using Variational Autoencoders and K-Nearest Neighbor OveRsampling Approach |
| dc.type.none.fl_str_mv | Text Journal contribution info:eu-repo/semantics/publishedVersion text contribution to journal |
| description | <p dir="ltr">Researchers gravitate towards Generative Adversarial Networks (GAN) to create artificial images. However, GANs suffer from convergence issues, mode collapse, and overall complexity in balancing the Nash Equilibrium. Images generated are often distorted, rendering them useless. We propose a combination of Variational Autoencoders (VAEs) and a statistical oversampling method called K-Nearest Neighbor OveRsampling (KNNOR) to create artificial images. This combination of VAE and KNNOR results in more life-like images with reduced distortion. We fine-tune several pre-trained networks on a separate set of real and fake face images to test images generated by our method against images generated by conventional Deep Convolutional GANs (DCGANs). We also compare the combination of VAEs and Synthetic Minority Oversampling Technique (SMOTE) to establish the efficacy of KNNOR against naive oversampling methods. Not only are our methods better able to convince the classifiers that the images generated are authentic, but the models are also half in size of DCGANs. The code is available at GitHub for public use.</p><h2>Other Information</h2><p dir="ltr">Published in: IEEE Access<br>License: <a href="http://creativecommons.org/licenses/by/4.0" target="_blank">http://creativecommons.org/licenses/by/4.0</a><br>See article on publisher's website: <a href="https://dx.doi.org/10.1109/access.2023.3259236" target="_blank">https://dx.doi.org/10.1109/access.2023.3259236</a></p> |
| eu_rights_str_mv | openAccess |
| id | Manara2_a604c52ec5c08aa216b277485221cc3c |
| identifier_str_mv | 10.1109/access.2023.3259236 |
| network_acronym_str | Manara2 |
| network_name_str | Manara2 |
| oai_identifier_str | oai:figshare.com:article/25204208 |
| publishDate | 2023 |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| rights_invalid_str_mv | CC BY 4.0 |
| spelling | Fast and Efficient Image Generation Using Variational Autoencoders and K-Nearest Neighbor OveRsampling ApproachAshhadul Islam (16869981)Samir Brahim Belhaouari (9427347)EngineeringElectrical engineeringElectronics, sensors and digital hardwareMaterials engineeringGeneratorsFeature extractionGenerative adversarial networksDecodingTrainingNash equilibriumImage synthesisFace recognitionvariational autoencodersImage reconstructionartificial image creation<p dir="ltr">Researchers gravitate towards Generative Adversarial Networks (GAN) to create artificial images. However, GANs suffer from convergence issues, mode collapse, and overall complexity in balancing the Nash Equilibrium. Images generated are often distorted, rendering them useless. We propose a combination of Variational Autoencoders (VAEs) and a statistical oversampling method called K-Nearest Neighbor OveRsampling (KNNOR) to create artificial images. This combination of VAE and KNNOR results in more life-like images with reduced distortion. We fine-tune several pre-trained networks on a separate set of real and fake face images to test images generated by our method against images generated by conventional Deep Convolutional GANs (DCGANs). We also compare the combination of VAEs and Synthetic Minority Oversampling Technique (SMOTE) to establish the efficacy of KNNOR against naive oversampling methods. Not only are our methods better able to convince the classifiers that the images generated are authentic, but the models are also half in size of DCGANs. The code is available at GitHub for public use.</p><h2>Other Information</h2><p dir="ltr">Published in: IEEE Access<br>License: <a href="http://creativecommons.org/licenses/by/4.0" target="_blank">http://creativecommons.org/licenses/by/4.0</a><br>See article on publisher's website: <a href="https://dx.doi.org/10.1109/access.2023.3259236" target="_blank">https://dx.doi.org/10.1109/access.2023.3259236</a></p>2023-03-20T03:00:00ZTextJournal contributioninfo:eu-repo/semantics/publishedVersiontextcontribution to journal10.1109/access.2023.3259236https://figshare.com/articles/journal_contribution/Fast_and_Efficient_Image_Generation_Using_Variational_Autoencoders_and_K-Nearest_Neighbor_OveRsampling_Approach/25204208CC BY 4.0info:eu-repo/semantics/openAccessoai:figshare.com:article/252042082023-03-20T03:00:00Z |
| spellingShingle | Fast and Efficient Image Generation Using Variational Autoencoders and K-Nearest Neighbor OveRsampling Approach Ashhadul Islam (16869981) Engineering Electrical engineering Electronics, sensors and digital hardware Materials engineering Generators Feature extraction Generative adversarial networks Decoding Training Nash equilibrium Image synthesis Face recognition variational autoencoders Image reconstruction artificial image creation |
| status_str | publishedVersion |
| title | Fast and Efficient Image Generation Using Variational Autoencoders and K-Nearest Neighbor OveRsampling Approach |
| title_full | Fast and Efficient Image Generation Using Variational Autoencoders and K-Nearest Neighbor OveRsampling Approach |
| title_fullStr | Fast and Efficient Image Generation Using Variational Autoencoders and K-Nearest Neighbor OveRsampling Approach |
| title_full_unstemmed | Fast and Efficient Image Generation Using Variational Autoencoders and K-Nearest Neighbor OveRsampling Approach |
| title_short | Fast and Efficient Image Generation Using Variational Autoencoders and K-Nearest Neighbor OveRsampling Approach |
| title_sort | Fast and Efficient Image Generation Using Variational Autoencoders and K-Nearest Neighbor OveRsampling Approach |
| topic | Engineering Electrical engineering Electronics, sensors and digital hardware Materials engineering Generators Feature extraction Generative adversarial networks Decoding Training Nash equilibrium Image synthesis Face recognition variational autoencoders Image reconstruction artificial image creation |