NEURAL NETWORK SYNTHESIS ARCHITECTURE USING ENCODER-DECODER MODELS

Disclosed techniques include neural network architecture using encoder-decoder models. A facial image is obtained for processing on a neural network. The facial image includes unpaired facial image attributes. The facial image is processed through a first encoder-decoder pair and a second encoder-de...

Ausführliche Beschreibung

Gespeichert in:
Bibliographische Detailangaben
Hauptverfasser: Mishra, Taniya, Banerjee, Sandipan, Joshi, Ajjen Das
Format: Patent
Sprache:eng
Schlagworte:
Online-Zugang:Volltext bestellen
Tags: Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
container_end_page
container_issue
container_start_page
container_title
container_volume
creator Mishra, Taniya
Banerjee, Sandipan
Joshi, Ajjen Das
description Disclosed techniques include neural network architecture using encoder-decoder models. A facial image is obtained for processing on a neural network. The facial image includes unpaired facial image attributes. The facial image is processed through a first encoder-decoder pair and a second encoder-decoder pair. The first encoder-decoder pair decomposes a first image attribute subspace. The second encoder-decoder pair decomposes a second image attribute subspace. The first encoder-decoder pair outputs a transformation mask based on the first image attribute subspace. The second encoder-decoder pair outputs a second image transformation mask based on the second image attribute subspace. The first image transformation mask and the second image transformation mask are concatenated to enable downstream processing. The concatenated transformation masks are processed on a third encoder-decoder pair and a resulting image is output. The resulting image eliminates a paired training data requirement.
format Patent
fullrecord <record><control><sourceid>epo_EVB</sourceid><recordid>TN_cdi_epo_espacenet_US2022067519A1</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><sourcerecordid>US2022067519A1</sourcerecordid><originalsourceid>FETCH-epo_espacenet_US2022067519A13</originalsourceid><addsrcrecordid>eNrjZHDycw0NcvRR8HMNCfcP8lYIjvQL8XAN9gxWcAxy9vAMcXUOCQ1yVQgN9vRzV3D1c_Z3cQ3SdXEF0wq-QNInmIeBNS0xpziVF0pzMyi7uYY4e-imFuTHpxYXJCan5qWWxIcGGxkYGRmYmZsaWjoaGhOnCgDmhyyM</addsrcrecordid><sourcetype>Open Access Repository</sourcetype><iscdi>true</iscdi><recordtype>patent</recordtype></control><display><type>patent</type><title>NEURAL NETWORK SYNTHESIS ARCHITECTURE USING ENCODER-DECODER MODELS</title><source>esp@cenet</source><creator>Mishra, Taniya ; Banerjee, Sandipan ; Joshi, Ajjen Das</creator><creatorcontrib>Mishra, Taniya ; Banerjee, Sandipan ; Joshi, Ajjen Das</creatorcontrib><description>Disclosed techniques include neural network architecture using encoder-decoder models. A facial image is obtained for processing on a neural network. The facial image includes unpaired facial image attributes. The facial image is processed through a first encoder-decoder pair and a second encoder-decoder pair. The first encoder-decoder pair decomposes a first image attribute subspace. The second encoder-decoder pair decomposes a second image attribute subspace. The first encoder-decoder pair outputs a transformation mask based on the first image attribute subspace. The second encoder-decoder pair outputs a second image transformation mask based on the second image attribute subspace. The first image transformation mask and the second image transformation mask are concatenated to enable downstream processing. The concatenated transformation masks are processed on a third encoder-decoder pair and a resulting image is output. The resulting image eliminates a paired training data requirement.</description><language>eng</language><subject>CALCULATING ; COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS ; COMPUTING ; COUNTING ; HANDLING RECORD CARRIERS ; PHYSICS ; PRESENTATION OF DATA ; RECOGNITION OF DATA ; RECORD CARRIERS</subject><creationdate>2022</creationdate><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://worldwide.espacenet.com/publicationDetails/biblio?FT=D&amp;date=20220303&amp;DB=EPODOC&amp;CC=US&amp;NR=2022067519A1$$EHTML$$P50$$Gepo$$Hfree_for_read</linktohtml><link.rule.ids>230,308,776,881,25542,76289</link.rule.ids><linktorsrc>$$Uhttps://worldwide.espacenet.com/publicationDetails/biblio?FT=D&amp;date=20220303&amp;DB=EPODOC&amp;CC=US&amp;NR=2022067519A1$$EView_record_in_European_Patent_Office$$FView_record_in_$$GEuropean_Patent_Office$$Hfree_for_read</linktorsrc></links><search><creatorcontrib>Mishra, Taniya</creatorcontrib><creatorcontrib>Banerjee, Sandipan</creatorcontrib><creatorcontrib>Joshi, Ajjen Das</creatorcontrib><title>NEURAL NETWORK SYNTHESIS ARCHITECTURE USING ENCODER-DECODER MODELS</title><description>Disclosed techniques include neural network architecture using encoder-decoder models. A facial image is obtained for processing on a neural network. The facial image includes unpaired facial image attributes. The facial image is processed through a first encoder-decoder pair and a second encoder-decoder pair. The first encoder-decoder pair decomposes a first image attribute subspace. The second encoder-decoder pair decomposes a second image attribute subspace. The first encoder-decoder pair outputs a transformation mask based on the first image attribute subspace. The second encoder-decoder pair outputs a second image transformation mask based on the second image attribute subspace. The first image transformation mask and the second image transformation mask are concatenated to enable downstream processing. The concatenated transformation masks are processed on a third encoder-decoder pair and a resulting image is output. The resulting image eliminates a paired training data requirement.</description><subject>CALCULATING</subject><subject>COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS</subject><subject>COMPUTING</subject><subject>COUNTING</subject><subject>HANDLING RECORD CARRIERS</subject><subject>PHYSICS</subject><subject>PRESENTATION OF DATA</subject><subject>RECOGNITION OF DATA</subject><subject>RECORD CARRIERS</subject><fulltext>true</fulltext><rsrctype>patent</rsrctype><creationdate>2022</creationdate><recordtype>patent</recordtype><sourceid>EVB</sourceid><recordid>eNrjZHDycw0NcvRR8HMNCfcP8lYIjvQL8XAN9gxWcAxy9vAMcXUOCQ1yVQgN9vRzV3D1c_Z3cQ3SdXEF0wq-QNInmIeBNS0xpziVF0pzMyi7uYY4e-imFuTHpxYXJCan5qWWxIcGGxkYGRmYmZsaWjoaGhOnCgDmhyyM</recordid><startdate>20220303</startdate><enddate>20220303</enddate><creator>Mishra, Taniya</creator><creator>Banerjee, Sandipan</creator><creator>Joshi, Ajjen Das</creator><scope>EVB</scope></search><sort><creationdate>20220303</creationdate><title>NEURAL NETWORK SYNTHESIS ARCHITECTURE USING ENCODER-DECODER MODELS</title><author>Mishra, Taniya ; Banerjee, Sandipan ; Joshi, Ajjen Das</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-epo_espacenet_US2022067519A13</frbrgroupid><rsrctype>patents</rsrctype><prefilter>patents</prefilter><language>eng</language><creationdate>2022</creationdate><topic>CALCULATING</topic><topic>COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS</topic><topic>COMPUTING</topic><topic>COUNTING</topic><topic>HANDLING RECORD CARRIERS</topic><topic>PHYSICS</topic><topic>PRESENTATION OF DATA</topic><topic>RECOGNITION OF DATA</topic><topic>RECORD CARRIERS</topic><toplevel>online_resources</toplevel><creatorcontrib>Mishra, Taniya</creatorcontrib><creatorcontrib>Banerjee, Sandipan</creatorcontrib><creatorcontrib>Joshi, Ajjen Das</creatorcontrib><collection>esp@cenet</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Mishra, Taniya</au><au>Banerjee, Sandipan</au><au>Joshi, Ajjen Das</au><format>patent</format><genre>patent</genre><ristype>GEN</ristype><title>NEURAL NETWORK SYNTHESIS ARCHITECTURE USING ENCODER-DECODER MODELS</title><date>2022-03-03</date><risdate>2022</risdate><abstract>Disclosed techniques include neural network architecture using encoder-decoder models. A facial image is obtained for processing on a neural network. The facial image includes unpaired facial image attributes. The facial image is processed through a first encoder-decoder pair and a second encoder-decoder pair. The first encoder-decoder pair decomposes a first image attribute subspace. The second encoder-decoder pair decomposes a second image attribute subspace. The first encoder-decoder pair outputs a transformation mask based on the first image attribute subspace. The second encoder-decoder pair outputs a second image transformation mask based on the second image attribute subspace. The first image transformation mask and the second image transformation mask are concatenated to enable downstream processing. The concatenated transformation masks are processed on a third encoder-decoder pair and a resulting image is output. The resulting image eliminates a paired training data requirement.</abstract><oa>free_for_read</oa></addata></record>
fulltext fulltext_linktorsrc
identifier
ispartof
issn
language eng
recordid cdi_epo_espacenet_US2022067519A1
source esp@cenet
subjects CALCULATING
COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
COMPUTING
COUNTING
HANDLING RECORD CARRIERS
PHYSICS
PRESENTATION OF DATA
RECOGNITION OF DATA
RECORD CARRIERS
title NEURAL NETWORK SYNTHESIS ARCHITECTURE USING ENCODER-DECODER MODELS
url https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-02-08T23%3A37%3A50IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-epo_EVB&rft_val_fmt=info:ofi/fmt:kev:mtx:patent&rft.genre=patent&rft.au=Mishra,%20Taniya&rft.date=2022-03-03&rft_id=info:doi/&rft_dat=%3Cepo_EVB%3EUS2022067519A1%3C/epo_EVB%3E%3Curl%3E%3C/url%3E&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_id=info:pmid/&rfr_iscdi=true