{"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\/sq\/blog\/news\/hyperstyle-adaptacziya-sistemy-mashinnogo-obucheniya-stylegan-dlya-redaktirovaniya-izobrazhenij","title":{"rendered":"HyperStyle \u2014 adaptimi i sistemit t\u00eb m\u00ebsimit t\u00eb makinerive StyleGAN p\u00ebr redaktimin e imazheve","gt_translate_keys":[{"key":"rendered","format":"text"}]},"content":{"rendered":"

Nj\u00eb grup k\u00ebrkuesish nga Universiteti i Tel Avivit prezantoi HyperStyle, nj\u00eb variant i kund\u00ebrt i sistemit t\u00eb m\u00ebsimit t\u00eb makinerive StyleGAN2, i zhvilluar nga kompania NVIDIA, i strukturuar p\u00ebr t\u00eb rikrijuar pjes\u00ebt e munguara gjat\u00eb redaktimit t\u00eb imazheve reale. Kodi \u00ebsht\u00eb shkruar n\u00eb gjuh\u00ebn Python duke p\u00ebrdorur korniz\u00ebn PyTorch dhe shp\u00ebrndahet n\u00ebn licenc\u00ebn MIT. <\/p>\n

N\u00ebse StyleGAN mund\u00ebson sintez\u00ebn e fytyrave t\u00eb reja q\u00eb duken realiste, duke caktuar parametra t\u00eb till\u00eb si mosha, gjinia, gjat\u00ebsia e flok\u00ebve, karakteri i buz\u00ebqeshjes, forma e hund\u00ebs, ngjyra e l\u00ebkur\u00ebs, syzet dhe k\u00ebndin e fotografis\u00eb, HyperStyle ofron mund\u00ebsin\u00eb p\u00ebr t\u00eb ndryshuar parametrat e ngjash\u00ebm n\u00eb fotografi ekzistuese, pa ndryshuar tiparet e tyre karakteristike dhe duke ruajtur njohshm\u00ebrin\u00eb e fytyr\u00ebs origjinale. P\u00ebr shembull, me ndihm\u00ebn e HyperStyle, mund t\u00eb simulohet ndryshimi i mosh\u00ebs s\u00eb nj\u00eb personi n\u00eb fotografi, t\u00eb ndryshohet stili i flok\u00ebve, t\u00eb shtohen syze, mjekra ose mustaqe, t'i jepet imazhit pamja e nj\u00eb personazhi nga nj\u00eb kartoon ose piktur\u00eb, t\u00eb b\u00ebhet shprehja e fytyr\u00ebs e trishtuar ose e g\u00ebzuar. N\u00eb t\u00eb nj\u00ebjt\u00ebn koh\u00eb, sistemi mund t\u00eb st\u00ebrvitet jo vet\u00ebm p\u00ebr t\u00eb ndryshuar fytyrat e njer\u00ebzve, por edhe p\u00ebr objektet e tjera, p\u00ebr shembull, p\u00ebr redaktimin e imazheve t\u00eb makinave.

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

Metoda e propozuar ka si q\u00ebllim t\u00eb zgjidh\u00eb problemin e rikonstruksionit t\u00eb pjes\u00ebve t\u00eb munguara t\u00eb imazhit gjat\u00eb redaktimit. N\u00eb metodat e m\u00ebparshme t\u00eb propozuara, kompromisi midis rikonstruksionit dhe redaktueshm\u00ebris\u00eb \u00ebsht\u00eb zgjidhur p\u00ebrmes rregullimit t\u00eb im\u00ebt t\u00eb gjeneratorit t\u00eb imazheve p\u00ebr t\u00eb z\u00ebvend\u00ebsuar pjes\u00ebt e imazhit q\u00eb ka si q\u00ebllim gjat\u00eb rikrijimit t\u00eb zonave t\u00eb redaktueshme q\u00eb mungojn\u00eb origjinalisht. Nj\u00eb nga disavantazhet e k\u00ebtyre qasjeve \u00ebsht\u00eb nevoja p\u00ebr nj\u00eb st\u00ebrvitje t\u00eb gjat\u00eb t\u00eb synuar t\u00eb rrjetit nervor p\u00ebr \u00e7do imazh. <\/p>\n

Metoda e bazuar n\u00eb algoritmin StyleGAN ofron mund\u00ebsin\u00eb p\u00ebr t\u00eb p\u00ebrdorur nj\u00eb model tipik, t\u00eb trajnuar paraprakisht n\u00eb koleksione t\u00eb p\u00ebrgjithshme imazhesh, p\u00ebr t\u00eb gjeneruar elemente q\u00eb i p\u00ebrkasin imazhit origjinal me nj\u00eb nivel sakt\u00ebsie t\u00eb krahasuesh\u00ebm me algoritmet q\u00eb k\u00ebrkojn\u00eb trajnimin individual t\u00eb modelit p\u00ebr \u00e7do imazh. Nj\u00eb avantazh i metod\u00ebs s\u00eb re \u00ebsht\u00eb gjithashtu mund\u00ebsia p\u00ebr t\u00eb modifikuar imazhet me nj\u00eb rendiment t\u00eb af\u00ebrt me at\u00eb t\u00eb koh\u00ebs reale.

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

Modelet e trajnuara jan\u00eb p\u00ebrgatitur p\u00ebr fytyrat e njer\u00ebzve, makinat dhe kafsh\u00ebt mbi baza t\u00eb koleksioneve Flickr-Faces-HQ (FFHQ, 70 mij\u00eb imazhe me cil\u00ebsi t\u00eb lart\u00eb PNG t\u00eb fytyrave t\u00eb njer\u00ebzve), Stanford Cars (16 mij\u00eb imazhe makinash) dhe AFHQ (fotografi kafsh\u00ebsh). Gjithashtu \u00ebsht\u00eb ofruar nj\u00eb veg\u00ebl p\u00ebr trajnimin e modeleve t\u00eb veta, si dhe modele t\u00eb trajnuara q\u00eb mund t\u00eb p\u00ebrdoren me to, p\u00ebrfshir\u00eb kodifikues dhe gjenerues. P\u00ebr shembull, jan\u00eb t\u00eb disponueshme gjenerator\u00eb p\u00ebr t\u00eb krijuar imazhe n\u00eb stilin Toonify, karaktere Pixar, skica dhe madje edhe p\u00ebr stilizimin si princeshat nga filmat animatografik\u00eb t\u00eb Disney-t.

\"HyperStyle<\/center>
\"HyperStyle<\/center>
\"HyperStyle<\/center>
\"HyperStyle<\/center>
\n
Burimi: 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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