IMAGE STYLE TRANSFER USING MACHINE LEARNING.
The principle of Image style transfer is todefine two distance functions, one that describes the content image and the other that describes the style Image. By using these content and Style Images[5][6] as inputs we will be getting the desired output which has the content image merged with style ima...
| Publicado en: | Proteus Vol. 13; no. 10; pp. 35 - 41 |
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| Autores principales: | , , , , |
| Formato: | Artículo |
| Publicado: |
Proteus
Oct2022
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| Materias: | |
| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ssf&AN=160270473&site=ehost-live header: @attributes: shortDbName: ssf uiTerm: 160270473 longDbName: Social Sciences Full Text (H.W. Wilson) uiTag: AN controlInfo: bkinfo: jinfo: jid: 08896348 THQ jtl: Proteus issn: 08896348 maglogo: N pubinfo: dt: Oct2022 vid: 13 iid: 10 pid: 73634 pub: Proteus artinfo: ui: 160270473 ppf: 35 ppct: 6 formats: fmt: @attributes: type: P size: 859KB tig: atl: IMAGE STYLE TRANSFER USING MACHINE LEARNING. aug: au: Laxmaiah, Bagam Narasimharao, Jonnadula Maringanti, Abhigna Govindula, SriTeja Dharamsoth, Suresh affil: Associate Professor, CMR Technical Campus, Hyderabad, Telangana, India U.G Student, CMR Technical Campus, Hyderabad, Telangana, India su: Machine tools Machine learning Convolutional neural networks sug: subj: Industrial Machinery and Equipment Merchant Wholesalers Machine Tool Manufacturing Cutting Tool and Machine Tool Accessory Manufacturing All other building equipment contractors Machine tools Machine learning Convolutional neural networks keyword: Content Image Content Loss Convolutional Neural Networks Gram Matrix Deep Lab Semantic Segmentation Style Image Style Loss Content Image Content Loss Convolutional Neural Networks Gram Matrix Deep Lab Semantic Segmentation Style Image Style Loss ab: The principle of Image style transfer is todefine two distance functions, one that describes the content image and the other that describes the style Image. By using these content and Style Images[5][6] as inputs we will be getting the desired output which has the content image merged with style image. The ouput will be in the graphical model of the content image. In summary, we'll take the base input image, a contentimage that we want to match, the style image that we want to match by undergoing the process of convolutional neural network [7] firstly the content image is undergoing the process of content loss and style image as style loss after content loss and style lossit will undergo the process of gram matrix and the final image will be formed. pubtype: Academic Journal doctype: Article src: R language: English refInfo: copyright: @attributes: flag: N holdings: @attributes: islocal: N |
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