论文标题
基于PCA的知识蒸馏对轻质和内容风格平衡的影子风格转移模型
PCA-Based Knowledge Distillation Towards Lightweight and Content-Style Balanced Photorealistic Style Transfer Models
论文作者
论文摘要
逼真的样式转移需要将参考图像的样式传输到另一个图像，因此结果似乎是合理的照片。我们的工作灵感来自于观察到现有模型由于尺寸较大而缓慢的启发。我们介绍了基于PCA的知识蒸馏以提炼轻量级模型，并表明它是由理论动机。据我们所知，这是感性风格转移的第一种知识蒸馏方法。我们的实验证明了其在六个图像分辨率中与不同主链体系结构（VGG和Mobilenet）一起使用的多功能性。与现有模型相比，我们表现最佳的模型最多使用1 \％的参数以5-20x的速度运行。此外，与现有模型相比，我们的蒸馏型模型在定型强度和内容保存之间实现了更好的平衡。为了支持重现我们的方法和模型，我们在\ textit {https://github.com/chiutaiyin/pca-knowledge-distillation}上共享代码。
Photorealistic style transfer entails transferring the style of a reference image to another image so the result seems like a plausible photo. Our work is inspired by the observation that existing models are slow due to their large sizes. We introduce PCA-based knowledge distillation to distill lightweight models and show it is motivated by theory. To our knowledge, this is the first knowledge distillation method for photorealistic style transfer. Our experiments demonstrate its versatility for use with different backbone architectures, VGG and MobileNet, across six image resolutions. Compared to existing models, our top-performing model runs at speeds 5-20x faster using at most 1\% of the parameters. Additionally, our distilled models achieve a better balance between stylization strength and content preservation than existing models. To support reproducing our method and models, we share the code at \textit{https://github.com/chiutaiyin/PCA-Knowledge-Distillation}.
