对比五种CNN模型在印尼蜡染生成中的表现，发现ResNet更快更省资源。
Comparative Evaluation of CNN Architectures for Neural Style Transfer in Indonesian Batik Motif Generation: A Comprehensive Study
- 比较VGG16、VGG19、Inception V3、ResNet50、ResNet101在风格迁移中的表现
- ResNet比VGG快5-6倍，仅需不到1/16的计算量（0.63 vs 10.12 GFLOPs）
- ResNet适合资源受限环境，兼顾结构保留与生成效率
神经风格迁移（NST）为印尼蜡染纹样的数字保护与生成探索提供了计算框架；然而现有方法多依赖VGG架构，虽风格表达强但计算与内存开销大，限制了在资源受限环境中的部署。本研究基于245组受控实验，系统比较了五种常用CNN主干网络：VGG16、VGG19、Inception V3、ResNet50与ResNet101，综合定量指标、定性评估与统计分析，考察结构保留、风格表现与计算效率间的权衡。结果表明，主干选择对结构相似性无显著影响（ANOVA on SSIM，p=0.83），各模型结构保留能力相当；而ResNet系列收敛速度比VGG快5-6倍，感知相似度保持相近（LPIPS = 0.53），计算量减少超16倍（0.63 vs 10.12 GFLOPs）。定性分析显示：VGG生成更密集的绘画质感，ResNet强调几何稳定性与笔画方向保持，风格化较温和，Inception V3表现居中但噪声较多。研究重新定位了NST中架构选择的重点——从追求风格强度转向高效且保结构的部署，凸显基于ResNet的架构在可扩展、产业化的蜡染生成中的实用价值。
原文摘要 · Abstract (English)
Neural Style Transfer (NST) provides a computational framework for the digital preservation and generative exploration of Indonesian batik motifs; however, existing approaches remain largely centered on VGG-based architectures whose strong stylistic expressiveness comes at the cost of high computational and memory demands, that limits practical deployment in resource-limited environments. This study presents a systematic comparative analysis of five widely used CNN backbones, namely VGG16, VGG19, Inception V3, ResNet50, and ResNet101, based on 245 controlled experiments combining quantitative metrics, qualitative assessment, and statistical analysis to examine the trade-off between structural preservation, stylistic behavior, and computational efficiency. The results show that backbone selection does not yield statistically significant differences in structural similarity, as confirmed by ANOVA on SSIM (p= 0.83), indicating comparable levels of structural preservation rather than equivalent stylistic quality. Within this context, ResNet-based architectures achieve approximately 5-6x faster convergence than VGG models while maintaining similar perceptual similarity (LPIPS = 0.53) and requiring over 16x fewer FLOPs (0.63 vs 10.12 GFLOPs). Qualitative analysis reveals consistent stylistic trade-offs, with VGG producing denser painterly textures, ResNet favoring geometric stability and canting stroke preservation with milder stylization, and Inception V3 exhibiting intermediate but noisier behavior. These findings reposition architectural choice in NST from maximizing stylistic intensity toward efficiency-aware and structure-preserving deployment, highlighting ResNet-based backbones as a practical foundation for scalable, industry-oriented batik generation.
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