Çekişmeli üretici ağ ile ölçeklenebilir görüntü oluşturma ve süper çözünürlük

Yıl 2020, Cilt: 35 Sayı: 2, 953 - 966, 25.12.2019

Ceren Güzel Turhan Hasan Şakir Bilge

https://doi.org/10.17341/gazimmfd.587010

Cited By: 2

Öz

Çekişmeli
eğitimin derin öğrenme konusundaki ileri gelen araştırmacılar tarafından son 10
yılın en heyecan verici konularından biri olarak ilan edilmesi ile birlikte
birçok araştırmacı, Üretici Çekişmeli Ağı (GAN) üzerine yoğunlaşmıştır. Bu
gelişmeler ile birlikte Otokodlayıcı (AE) olarak daha önceden bilinen modelin
varyasyonel versiyonu olan Varyasyonel Otokodlayıcı (VAE) ile birlikte
Otokodlayıcı modeller yeniden önem kazanmıştır. GAN modellerinin bazı
kısıtlamaları, çıkarım mekanizması gibi bazı eksiklikleri, GAN ve VAE hibrit
modellerini ortaya çıkmıştır. Bu gelişmelerin etkisiyle, bu çalışmada da bir
GAN ve VAE hibrit modeline odaklanılmıştır. Sentetik görüntüler ile istenen
çözünürlükte elde edebilmek üzere, Kompozisyonel Örüntü Üreten Ağlar (CPPN) ile
GAN ve VAE modelleri bir arada kullanılmıştır. Bu çalışmada, VAE / GAN olarak
adlandırılmış bir hibrit modelinde kullanılan objektif fonksiyonu önerilen
model ile bir araya getirilmiştir. Deney çalışmalarında model performansının
değerlendirmek üzere VAE / CPGAN olarak adlandırdığımız model, temel
modelleriyle (GAN, VAE ve VAE / GAN) karşılaştırılmıştır. Bu amaçla, önerilen
modelin el yazısı görüntülerinden oluşan popüler veri kümesi üzerinde
gerçekleştirilen eğitim ile gerçekçi ve süper çözünürlüklü ölçeklenebilir
sentetik görüntüler oluşturmadaki başarısı gösterilmiştir.

Anahtar Kelimeler

Derin öğrenme, varyasyonel otokodlayıcı, üretici çekişmeli ağlar, görüntü oluşturma, ölçeklenebilir süper çözünürlük

Kaynakça

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