{"id":62988,"date":"2018-12-03T18:50:05","date_gmt":"2018-12-03T13:50:05","guid":{"rendered":"https:\/\/uzsoft.uz\/neyron-tarmogini-tasvirdagi-obyektni-biroz-aylantirish-orqali-aldash-mumkin\/"},"modified":"2018-12-03T18:50:05","modified_gmt":"2018-12-03T13:50:05","slug":"neyron-tarmogini-tasvirdagi-obyektni-biroz-aylantirish-orqali-aldash-mumkin","status":"publish","type":"post","link":"https:\/\/uzsoft.uz\/uz\/neyron-tarmogini-tasvirdagi-obyektni-biroz-aylantirish-orqali-aldash-mumkin\/","title":{"rendered":"Neyron tarmog’ini tasvirdagi obyektni biroz aylantirish orqali aldash mumkin."},"content":{"rendered":"

\"Neyron<\/p>\n

\n\t\t\tInception-v3 neyron tarmog’i bir xil obyektni vaqtning atigi 3 foizida to’g’ri taniydi. <\/em>\t\t<\/p>\n

Tasvirni aniqlash uchun ishlatiladigan konvolyutsion neyron tarmoqlarini aldash juda oson – shunchaki fotosuratdagi obyektning o’rnini biroz o’zgartiradi. Bu xulosaga Auburn universiteti (AQSh) va Adobe tadqiqotchilari jamoasi kelishdi, ular neyron tarmoqlarini bunday xatolar uchun sinash vositasini yaratdilar. Tasvirni aniqlash uchun neyron tarmoqlarining tobora ommalashib borayotganiga qaramay, mutaxassislar keng tarqalgan muammodan xavotirda: «raqib misollar» deb ataladigan obyektlarni aniqlashdagi xatolar. Bunday «raqib misollar» neyron tarmoqni chalg’itish uchun ataylab yaratilgan. Ular inson ko’ziga ko’rinmaydigan, ammo neyron tarmoq aniqlash uchun mo’ljallangan tasvirlarga nozik o’zgarishlar kiritadi. <\/p>\n

Auburn universiteti va Adobe tomonidan olib borilgan tadqiqotlar shuni ko’rsatdiki, hatto eng yaxshi algoritmlarni ham aldash mumkin – ular faqat 3% hollarda bir xil obyektni to’g’ri taniy oladi.<\/p>\n

Mutaxassislar tomonidan ishlab chiqilgan dasturiy ta’minot neyron tarmoqlarini bunday xatolar uchun sinash uchun mo’ljallangan. Undan fonga ega obyektni modellashtirish, uning holatini, yoritilishini va boshqa xususiyatlarini o’zgartirish uchun foydalanish mumkin. Parametrlarni o’rnatgandan so’ng, dastur ikki o’lchovli tasvirni yaratadi va undan neyron tarmoqni sinash uchun foydalanadi. <\/p>\n

Tadqiqotchilar o’z vositalarini Google’ning Inception-v3 neyron tarmog’ida sinab ko’rishga qaror qilishdi. Ular haydash paytida eng ko’p uchraydigan obyektlarning o’ttizta 3D modellarini tanlab olishdi. Har bir model uchun ular turli burchaklar va yorug’lik bilan bir qator variantlarni yaratdilar. <\/p>\n

Sinov natijalari shuni ko’rsatdiki, Inception-v3 bir xil obyektning tasvirlarini atigi 3,09% hollarda to’g’ri tanigan. Ob’ekt aylantirilganda ham, neyron tarmog’i atigi 8% noto’g’ri bo’lgan. <\/p>\n

Tadqiqot shuni ko’rsatdiki, hiyla-nayranglar turli ma’lumotlar to’plamlarida o’rgatilgan boshqa algoritmlarga ham qo’llanilishi mumkin. Masalan, asl neyron tarmog’i uchun mo’ljallangan hiyla-nayranglarning 99,9% AlexNet algoritmini ham aldagan. Xuddi shunday, tasvirlarning 75,5% MS COCO ma’lumotlar to’plamida o’rgatilgan algoritmni muvaffaqiyatli aldagan. <\/p>\n","protected":false},"excerpt":{"rendered":"

Inception-v3 neyron tarmog’i bir xil obyektni vaqtning atigi 3 foizida to’g’ri taniydi. Tasvirni aniqlash uchun ishlatiladigan konvolyutsion neyron tarmoqlarini aldash juda oson – shunchaki fotosuratdagi obyektning o’rnini biroz o’zgartiradi. Bu xulosaga Auburn universiteti (AQSh) va Adobe tadqiqotchilari jamoasi kelishdi, ular neyron tarmoqlarini bunday xatolar uchun sinash vositasini yaratdilar. Tasvirni aniqlash uchun neyron tarmoqlarining tobora ommalashib […]<\/p>\n","protected":false},"author":20,"featured_media":52118,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"_yoast_wpseo_meta-robots-noindex":"","_yoast_wpseo_meta-robots-nofollow":"","_yoast_wpseo_canonical":"","_yoast_wpseo_opengraph-title":"","_yoast_wpseo_opengraph-description":"","_yoast_wpseo_opengraph-image":"","_yoast_wpseo_opengraph-image-id":"","_yoast_wpseo_twitter-title":"","_yoast_wpseo_twitter-description":"","_yoast_wpseo_twitter-image":"","_yoast_wpseo_schema_page_type":"","_yoast_wpseo_schema_article_type":"","footnotes":""},"categories":[52855],"tags":[],"class_list":["post-62988","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-yangiliklar"],"featured_image_src":{"landsacpe":["https:\/\/uzsoft.uz\/wp-content\/uploads\/2018\/12\/9e9a40c5a22490c5848dc8572840f732.png",600,325,false],"list":["https:\/\/uzsoft.uz\/wp-content\/uploads\/2018\/12\/9e9a40c5a22490c5848dc8572840f732.png",463,251,false],"medium":["https:\/\/uzsoft.uz\/wp-content\/uploads\/2018\/12\/9e9a40c5a22490c5848dc8572840f732-300x163.png",300,163,true],"full":["https:\/\/uzsoft.uz\/wp-content\/uploads\/2018\/12\/9e9a40c5a22490c5848dc8572840f732.png",600,325,false]},"yoast_head":"\nNeyron 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