Baby Face Generation with Generative Adversarial Neural Networks: A Case Study

Yıl 2020, Cilt: 4 Sayı: 1, 1 - 9, 10.08.2020

Gizem Ortaç Zeliha Doğan Zeynep Orman Rüya Şamlı

Öz

Generative Adversarial Networks (GANs) are increasingly applied to train generative models with neural networks, especially in computer vision studies. Since being introduced in 2014, many image generation studies incorporating GANs have demonstrated promising results for producing highly convincing fake images of animals, landscapes, and human faces. We build a GAN structure to generate realistic baby face images from a small data set of 673 color 200×200 pixel images obtained from a Kaggle data set by following previous studies that demonstrated how GANs could be used for image generation from a limited number of training samples. The reason we limit especially as baby faces is that we aim to achieve success with a limited number of training data. For evaluation, experiments and case studies are one of the most considered techniques. The results of this study help identify issues requiring further investigation in comment analysis research. In this context, we presented the loss values of the generator and discriminator during the training process. The discriminator losses are around of 0.7 and the generator is between 0.7 and 0.9. The high quality images are produced about 300th epochs.

Anahtar Kelimeler

GANs, Image Generation, Face Generation

Çekişmeli Üretici Sinir Ağları ile Bebek Yüz Üretimi: Bir Vaka Çalışması

Öz

Çekişmeli Üretici Sinir Ağları (GAN) son zamanlarda özellikle bilgisayarlı görme çalışmalarında sinir ağlarına sahip üretken modelleri eğitmek için kullanılan popüler bir konudur. GAN’lar 2014 yılında araştırmacılara tanıtıldığından beri, özellikle GAN’larla görüntü oluşturma çalışmaları gittikçe artmaktadır. Bu çalışmalar, hayvanlar, manzaralar, insan yüzleri vb. gibi son derece ikna edici sahte görüntüler üretmek için umut verici sonuçlar elde etmiştir. Bu çalışmada gerçekçi yüz görüntüleri oluşturmak için bir GAN yapısı oluşturulması amaçlanmıştır. Daha az sayıda eğitim verisiyle gerçekçi resimler üretebilmek için veri seti içerisinde sadece bebek yüzleri kullanılmıştır. Çalışma kapsamında bir GAN yapısı inşa edilerek, Kaggle veri tabanından elde edilen 673 adet renkli 200x200 piksel boyutunda bebek yüz görüntüsü veri kümesinden yeni bebek yüzü görüntüleri oluşturulmaktadır. Önceki çalışmalar GAN’ların sınırlı sayıda eğitim örneği içeren veri kümeleri için görüntü oluşturmada kullanılabileceğini göstermektedir. Değerlendirme yöntemleri ile ilgili olarak, deneyler ve vaka çalışmaları en çok dikkate alınan tekniklerden biridir. Bu çalışmanın sonuçları, daha fazla araştırma yapılmasını gerektiren hususların belirlenmesine yardımcı olabilir.

Anahtar Kelimeler

Çekişmeli Üretici Ağlar, Resim Sentezleme, Yüz Resmi Sentezleme

Kaynakça

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