论文标题
大多数网络：面部素描综合的面向内存的样式转移网络
MOST-Net: A Memory Oriented Style Transfer Network for Face Sketch Synthesis
论文作者
论文摘要
面部草图合成已被广泛用于多媒体娱乐和执法。尽管深度神经网络最近发生了进展，但由于人脸的多样性和复杂性，准确而现实的面孔素描合成仍然是一项艰巨的任务。当前基于图像到图像翻译的面部草图合成在小型数据集时通常会遇到过度的问题。为了解决此问题，我们提出了面部绘制的端到端以内存的样式转移网络（最多），用于面部素描合成，该网络可以产生具有有限数据的高保真草图。具体而言，引入了外部自我监督的动态内存模块，以捕获域对准知识。这样，我们提出的模型可以通过在特征级别上建立面部和相应草图之间的持久关系来获得域转移能力。此外，我们为记忆模块中的特征比对设计了一种新颖的记忆细化损失（MR损失），该功能对齐以无监督的方式增强了记忆插槽的准确性。在CUFS和CUFSF数据集上进行了广泛的实验表明，我们最网络可以实现最先进的性能，尤其是在结构相似性指数（SSIM）方面。
Face sketch synthesis has been widely used in multi-media entertainment and law enforcement. Despite the recent developments in deep neural networks, accurate and realistic face sketch synthesis is still a challenging task due to the diversity and complexity of human faces. Current image-to-image translation-based face sketch synthesis frequently encounters over-fitting problems when it comes to small-scale datasets. To tackle this problem, we present an end-to-end Memory Oriented Style Transfer Network (MOST-Net) for face sketch synthesis which can produce high-fidelity sketches with limited data. Specifically, an external self-supervised dynamic memory module is introduced to capture the domain alignment knowledge in the long term. In this way, our proposed model could obtain the domain-transfer ability by establishing the durable relationship between faces and corresponding sketches on the feature level. Furthermore, we design a novel Memory Refinement Loss (MR Loss) for feature alignment in the memory module, which enhances the accuracy of memory slots in an unsupervised manner. Extensive experiments on the CUFS and the CUFSF datasets show that our MOST-Net achieves state-of-the-art performance, especially in terms of the Structural Similarity Index(SSIM).
