{"id":102454,"date":"2021-12-03T15:36:38","date_gmt":"2021-12-03T13:36:42","guid":{"rendered":"https:\/\/prohoster.info\/blog\/novosti-interneta\/hyperstyle-adaptacziya-sistemy-mashinnogo-obucheniya-stylegan-dlya-redaktirovaniya-izobrazhenij"},"modified":"2021-12-03T15:36:38","modified_gmt":"2021-12-03T13:36:42","slug":"hyperstyle-adaptacziya-sistemy-mashinnogo-obucheniya-stylegan-dlya-redaktirovaniya-izobrazhenij","status":"publish","type":"post","link":"https:\/\/prohoster.info\/et\/blog\/news\/hyperstyle-adaptacziya-sistemy-mashinnogo-obucheniya-stylegan-dlya-redaktirovaniya-izobrazhenij","title":{"rendered":"HyperStyle \u2014 masin\u00f5ppe s\u00fcsteemi StyleGAN kohandamine piltide redigeerimiseks","gt_translate_keys":[{"key":"rendered","format":"text"}]},"content":{"rendered":"

Tel-Avivi \u00fclikooli teadlaste r\u00fchm tutvustas HyperStyle'i, NVIDIA arendatava masin\u00f5ppe s\u00fcsteemi StyleGAN2 p\u00f6\u00f6ratud varianti, mis on kohandatud joonistatud piltide redigeerimise k\u00e4igus puuduvate osade taastamiseks. Kood on kirjutatud Pythonis, kasutades PyTorchi raamistikku, ning seda levitatakse MIT litsentsi alusel. <\/p>\n

Kui StyleGAN v\u00f5imaldab s\u00fcnteesida realistlikult v\u00e4lja n\u00e4gevaid uusi inimeste n\u00e4gusid, m\u00e4\u00e4rates sellised parameetrid nagu vanus, sugu, juuste pikkus, naeratuse iseloom, nina kuju, naha v\u00e4rv, prillid ja foto nurk, siis HyperStyle v\u00f5imaldab muuta sarnaseid parameetreid juba olemasolevates fotodes, muutes nende iseloomulike joonte loomulikkust ja s\u00e4ilitades algse n\u00e4o \u00e4ratavuse. N\u00e4iteks saab HyperStyle'i abil simuleerida inimese vanuse muutumist fotol, vahetada soengut, lisada prille, habet v\u00f5i vuntsi, anda pildile joonistatud karakteri ilme v\u00f5i muuta n\u00e4o v\u00e4ljend kurvaks v\u00f5i r\u00f5\u00f5msaks. Samuti v\u00f5ib s\u00fcsteemi \u00f5petada mitte ainult inimeste n\u00e4gude, vaid ka igasuguste esemete, n\u00e4iteks autode, piltide redigeerimiseks.

\"HyperStyle<\/center> <\/p>\n

Pakutud meetod on suunatud puuduvate osade rekonstrueerimise probleemile piltide redigeerimise k\u00e4igus. Varasemates ettepanekutes lahendati kompromiss rekonstruktsiooni ja redigeeritavuse vahel, kohandades pildigeneraatori t\u00f6\u00f6parameetreid, et asendada sihtpildi osi, taastades algselt puuduvate redigeeritavate piirkondade. Selliste l\u00e4henemiste puudus on vajadus viia l\u00e4bi pikaajaline sihtm\u00f5tte \u00f5pe n\u00e4rviv\u00f5rgu jaoks iga pildi jaoks. <\/p>\n

StyleGAN-algoritmil p\u00f5hinev meetod v\u00f5imaldab kasutada t\u00fc\u00fcpilist mudelit, mis on eelnevalt treenitud \u00fcldiste pildikogude peal, et genereerida originaalpildiga sarnaseid elemente t\u00f5etruuduse tasemega, mis on v\u00f5rreldav mudelite individuaalse treeningu vajavate algoritmidega iga pildi jaoks. Uue meetodi eeliste seas on ka v\u00f5imalus pilte modifitseerida, l\u00e4hedaste tegelike aegade tootlikkusele.

\"HyperStyle<\/center> <\/p>\n

Valmis treenitud mudelid on ette valmistatud inimeste, autode ja loomade n\u00e4gude jaoks, p\u00f5hinedes Flickr-Faces-HQ (FFHQ, 70 tuhat kvaliteetset PNG-pilti inimeste n\u00e4gudest), Stanford Cars (16 tuhat auto pilti) ja AFHQ (loomade fotod) kogudele. Lisaks on saadaval t\u00f6\u00f6riistakomplekt oma mudelite treenimiseks ning nende jaoks sobivad valmis treenitud mudelid t\u00fc\u00fcpiliste kodeerijate ja generaatorite jaoks. N\u00e4iteks on saadaval generaatorid Toonify stiilis piltide loomiseks, Pixar karakterite genereerimiseks, visandite loomiseks ja isegi Disney multikate printside stiilide esitlemiseks.

\"HyperStyle<\/center>
\"HyperStyle<\/center>
\"HyperStyle<\/center>
\"HyperStyle<\/center>
\n
Allikas: opennet.ru<\/a> <\/p>","protected":false,"gt_translate_keys":[{"key":"rendered","format":"html"}]},"excerpt":{"rendered":"

\u0413\u0440\u0443\u043f\u043f\u0430 \u0438\u0441\u0441\u043b\u0435\u0434\u043e\u0432\u0430\u0442\u0435\u043b\u0435\u0439 \u0438\u0437 \u0422\u0435\u043b\u044c-\u0410\u0432\u0438\u0432\u0441\u043a\u043e\u0433\u043e \u0443\u043d\u0438\u0432\u0435\u0440\u0441\u0438\u0442\u0435\u0442\u0430 \u043f\u0440\u0435\u0434\u0441\u0442\u0430\u0432\u0438\u043b\u0430 HyperStyle, \u0438\u043d\u0432\u0435\u0440\u0442\u0438\u0440\u043e\u0432\u0430\u043d\u043d\u044b\u0439 \u0432\u0430\u0440\u0438\u0430\u043d\u0442 \u0440\u0430\u0437\u0432\u0438\u0432\u0430\u0435\u043c\u043e\u0439 \u043a\u043e\u043c\u043f\u0430\u043d\u0438\u0435\u0439 NVIDIA \u0441\u0438\u0441\u0442\u0435\u043c\u044b \u043c\u0430\u0448\u0438\u043d\u043d\u043e\u0433\u043e \u043e\u0431\u0443\u0447\u0435\u043d\u0438\u044f StyleGAN2, \u043a\u043e\u0442\u043e\u0440\u044b\u0439 \u043f\u0435\u0440\u0435\u0440\u0430\u0431\u043e\u0442\u0430\u043d \u0434\u043b\u044f \u0432\u043e\u0441\u0441\u043e\u0437\u0434\u0430\u043d\u0438\u044f \u043d\u0435\u0434\u043e\u0441\u0442\u0430\u044e\u0449\u0438\u0445 \u0447\u0430\u0441\u0442\u0435\u0439 \u043f\u0440\u0438 \u0440\u0435\u0434\u0430\u043a\u0442\u0438\u0440\u043e\u0432\u0430\u043d\u0438\u0438 \u0440\u0435\u0430\u043b\u044c\u043d\u044b\u0445 \u0438\u0437\u043e\u0431\u0440\u0430\u0436\u0435\u043d\u0438\u0439. \u041a\u043e\u0434 \u043d\u0430\u043f\u0438\u0441\u0430\u043d \u043d\u0430 \u044f\u0437\u044b\u043a\u0435 Python c \u0438\u0441\u043f\u043e\u043b\u044c\u0437\u043e\u0432\u0430\u043d\u0438\u0435\u043c \u0444\u0440\u0435\u0439\u043c\u0432\u043e\u0440\u043a\u0430 PyTorch \u0438 \u0440\u0430\u0441\u043f\u0440\u043e\u0441\u0442\u0440\u0430\u043d\u044f\u0435\u0442\u0441\u044f \u043f\u043e\u0434 \u043b\u0438\u0446\u0435\u043d\u0437\u0438\u0435\u0439 MIT. \u0415\u0441\u043b\u0438 StyleGAN \u043f\u043e\u0437\u0432\u043e\u043b\u044f\u0435\u0442 \u0441\u0438\u043d\u0442\u0435\u0437\u0438\u0440\u043e\u0432\u0430\u0442\u044c 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