A COMPARATIVE EVALUATION OF CNN ARCHITECTURES FOR SINGLE-IMAGE GANS

Öz

Generative Adversarial Networks (GANs) have achieved remarkable success in image synthesis, enabling the generation of photorealistic and diverse visual content. While most generative models depend on large datasets to capture visual variability, single-image GANs such as SinGAN demonstrate that rich generative behavior can emerge from the internal patch statistics of a single natural image. However, the effect of convolutional neural network (CNN) backbones on single-image generative performance remains underexplored. This study presents a comparative analysis of five CNN architectures—Inception, ResNet, DenseNet, CBAM, and MobileNet—integrated into the SinGAN framework to investigate their influence on image quality, diversity, and computational efficiency. Each architecture was trained under identical multi-scale SinGAN settings using 15 natural images, and evaluated with Single-Image Fréchet Inception Distance (SIFID), Multi-Scale Structural Similarity (MS-SSIM), and Learned Perceptual Image Patch Similarity (LPIPS), complemented by qualitative visual assessment. The results reveal consistent trade-offs among backbones: ResNet best preserves global structural coherence; DenseNet maximizes fine-detail diversity through dense feature reuse; CBAM enhances perceptual realism via attention module; Inception provides balanced multi-scale feature representation; and MobileNet achieves strong diversity and quality with favorable computational efficiency. These findings demonstrate that architectural design fundamentally governs generative behavior in single-image GANs. The study provides empirical insights and practical guidelines for selecting CNN backbones based on trade-offs between quality, diversity, and efficiency-supporting the design of data-efficient generative models for real-world and resource-constrained applications.

Anahtar Kelimeler

• Single-image GAN
• SinGAN
• CNN architectures
• image synthesis
• SIFID
• MS-SSIM
• LPIPS

TEK GÖRÜNTÜ TABANLI GAN’LAR İÇİN CNN MİMARİLERİNİN KARŞILAŞTIRMALI DEĞERLENDİRMESİ

Öz

Üretken Çekişmeli Ağlar (Generative Adversarial Networks, GANs), görüntü sentezinde olağanüstü bir başarı elde ederek fotogerçekçi ve çeşitli görsel içeriklerin üretilmesini mümkün kılmıştır. Çoğu üretken model, görsel çeşitliliği yakabilmek için büyük veri kümelerine ihtiyaç duyarken, SinGAN gibi tek görüntü tabanlı GAN'lar, zengin üretken davranışların yalnızca tek bir doğal görüntünün iç yama istatistiklerinden öğrenilebileceğini göstermiştir. Ancak, evrişimli sinir ağı (Convolutional Neural Network, CNN) omurgaların tek görüntü tabanlı üretken performans üzerindeki etkisi yeterince araştırılmamıştır. Bu çalışma, SinGAN çerçevesine entegre edilen beş farklı CNN mimarisinin (Inception, ResNet, DenseNet, CBAM ve MobileNet) görüntü kalitesi, çeşitlilik ve hesaplama verimliliği üzerindeki etkilerini inceleyen sistematik bir karşılaştırmalı analiz sunmaktadır. Her mimari, 15 doğal görüntü kullanılarak aynı çok ölçekli SinGAN ayarları altında eğitilmiş ve Tek Görüntü Fréchet Inception Mesafesi (SIFID), Çok Ölçekli Yapısal Benzerlik (MS-SSIM) ve Öğrenilmiş Algısal Görüntü Yama Benzerliği (LPIPS) ile değerlendirilmiş, niteliksel görsel değerlendirme ile desteklenmiştir. Elde edilen sonuçlar, mimariler arasında tutarlı ödünleşmeler olduğunu göstermektedir: ResNet, küresel yapısal bütünlüğü en iyi koruyan mimaridir; DenseNet, yoğun özellik yeniden kullanımıyla ince ayrıntı çeşitliliğini en üst düzeye çıkarır; CBAM, dikkat mekanizması aracılığıyla algısal gerçekçiliği artırır; Inception, çok ölçekli özellik temsili açısından dengeli performans sunar; MobileNet ise güçlü çeşitlilik ve yüksek kaliteyi üstün hesaplama verimliliği ile birleştirir. Bu bulgular, mimari tasarımın tek görüntü tabanlı GAN'larda üretken davranışı temelden şekillendirdiğini doğrulamaktadır. Çalışma, görüntü kalitesi, çeşitlilik ve verimlilik arasındaki ödünleşmelere dayalı olarak uygun CNN omurga seçimi için ampirik içgörüler ve pratik rehberler sunmakta, gerçek dünya ve kaynak kısıtlı uygulamalar için veri verimli üretken modellerin geliştirilmesine katkı sağlamaktadır.

Anahtar Kelimeler

• Tek görüntü GAN
• SinGAN
• CNN mimarileri
• görüntü sentezi
• SIFID
• MS-SSIM
• LPIPS

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