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Derin Öğrenme Modellerinin Sinirsel Stil Aktarımı Performanslarının Karşılaştırılması

Sinirsel stil aktarımı günümüzde hem akademik hemde endüstriyel alanda en fazla çalışılan konulardan biridir. Yapılan çalışmalarda kalite ve performans artırımı en çok amaçlanan hedeflerdendir. Bu çalışmada da farklı ESA modellerinin sinirsel stil aktarımında performansları incelenmiştir. VGG16, VGG19 ve ResNet50 modelleri kullanarak derin öznitelikler elde edilmiştir. Bu öznitelikler sayesinde içerik görüntüsünün içeriğini, stil görüntüsünün de stilini alıp yeni bir hedef görüntü oluşturulmuştur. Adam, RMSprop ve SGD optimizasyon algoritmaları kullanılmıştır. Yapılan sinirsel sinir aktarımı çalışmalarında, görsel açıdan en iyi performans VGG19 ağ modelinden SGD optimizasyon algoritması kullanılarak elde edilmiştir. Zaman açısından en hızlı sinirsel stil aktarımı ResNet50 evrişimsel sinir ağı modelinde SGD optimizasyon algoritması kullanılarak elde edilmiştir.

Anahtar Kelimeler:

Sinirsel stil aktarımı, evrişimli sinir ağı, derin evrişimli sinir ağı

Comparison of Neural Style Transfer Performance of Deep Learning Models

Neural style transfer is one of the most studied topics in both academic and industrial fields. Quality and performance enhancement are among the most targeted goals in the studies. In this study, the performance of different CNN models in neural style transfer was investigated. Deep features were obtained using VGG16, VGG19 and ResNet50 models. Thanks to these attributes, a new target image is created by taking the content of the content image and the style of the style image. Adam, RMSprop and SGD optimization algorithms are used. In neural transfer studies, the best visual performance was obtained from VGG19 network model by using SGD optimization algorithm. The fastest neural style transfer in terms of time was obtained using the SGD optimization algorithm in the ResNet50 convolutional neural network model.

Keywords:

Neural style transfer, convolutional neural network, deep convolutional neural network,

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