论文标题
挖掘自相似性：标签超级分辨率，带有表面代表
Mining self-similarity: Label super-resolution with epitomic representations
论文作者
论文摘要
我们表明，简单的基于贴片的模型（例如Epotomes）可以在语义分割和标签超分辨率中具有出色的性能，该模型使用深度卷积神经网络。我们针对一个新型的缩影得出了一种新的训练算法，该算法首次允许从非常大的数据集中学习，并得出标签超分辨率算法，作为统计推断算法，而不是表现量表。我们说明了关于土地覆盖映射和医学图像分析任务的方法。
We show that simple patch-based models, such as epitomes, can have superior performance to the current state of the art in semantic segmentation and label super-resolution, which uses deep convolutional neural networks. We derive a new training algorithm for epitomes which allows, for the first time, learning from very large data sets and derive a label super-resolution algorithm as a statistical inference algorithm over epitomic representations. We illustrate our methods on land cover mapping and medical image analysis tasks.
