论文标题
使用切片的瓦斯坦损失，神经纹理合成的远距离限制
Long Range Constraints for Neural Texture Synthesis Using Sliced Wasserstein Loss
论文作者
论文摘要
在过去的十年中，通过匹配深卷积神经网络的统计数据，基于典范的纹理合成算法在性能方面取得了很大的提高。但是，这些算法需要正则化项或用户添加的空间标签来捕获图像中的远距离约束。在所有情况下访问用户添加的空间标签并不总是可行的，并且正规化项可能很难调整。因此，我们提出了一组基于切成薄片的Wasserstein损失的纹理合成的新统计数据，创建了一种多规模方法，以合成纹理，而无需用户添加的空间标签，研究我们提出的方法捕获远距离约束的能力，并将我们的结果与其他基于基于优化的单个文本合成算法进行比较。
In the past decade, exemplar-based texture synthesis algorithms have seen strong gains in performance by matching statistics of deep convolutional neural networks. However, these algorithms require regularization terms or user-added spatial tags to capture long range constraints in images. Having access to a user-added spatial tag for all situations is not always feasible, and regularization terms can be difficult to tune. Thus, we propose a new set of statistics for texture synthesis based on Sliced Wasserstein Loss, create a multi-scale method to synthesize textures without a user-added spatial tag, study the ability of our proposed method to capture long range constraints, and compare our results to other optimization-based, single texture synthesis algorithms.
