Abstract
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.
Abstract (translated)
神经风格迁移（NST）为印尼蜡染图案的数字保存和生成探索提供了一个计算框架；然而，现有的方法主要集中在基于VGG的架构上，虽然这种架构具有强大的表现力，但其高计算和内存需求限制了它在资源受限环境中的实际部署。这项研究对五种广泛使用的CNN骨干网络（即VGG16、VGG19、Inception V3、ResNet50和ResNet101）进行了系统的比较分析，基于245次有控制的实验结合定量指标、定性评估以及统计分析来考察结构保留、风格行为及计算效率之间的权衡。研究结果表明，在结构相似度方面（由SSIM的ANOVA测试得出p=0.83确认），骨干网络的选择并没有显示出显著差异，这说明不同模型之间具有相当接近的结构保存水平，并非同等的质量表现。 在这种背景下，ResNet架构比VGG模型快约5-6倍收敛速度，同时保持了相似的感知相似性（LPIPS = 0.53）和减少了16倍以上的浮点运算次数（FLOPs：分别为0.63 vs 10.12 Giga FLOPs）。定性分析表明存在一致性的风格权衡问题，VGG模型产生了更密集的绘画纹理，ResNet则倾向于几何稳定性及蜡笔线条保存，并且其风格化效果较为温和；而Inception V3展示了一种介于两者之间的但噪音更大的行为。 这些发现重新定位了NST中的架构选择，从最大化风格强度转向效率感知和结构保留的部署。研究结果强调基于ResNet的骨干网络作为具有规模性和行业导向性的蜡染生成实用基础的重要性。
URL
https://arxiv.org/abs/2601.00888
