Deep convolutional neural networks have been successfully applied to many image-processing problems in recent works. Popular network architectures often add additional operations and connections to the standard architecture to enable training deeper networks. To achieve accurate results in practice, a large number of trainable parameters are often required. Here, we introduce a network architecture based on using dilated convolutions to capture features at different image scales and densely connecting all feature maps with each other. The resulting architecture is able to achieve accurate results with relatively few parameters and consists of a single set of operations, making it easier to implement, train, and apply in practice, and automatically adapts to different problems. We compare results of the proposed network architecture with popular existing architectures for several segmentation problems, showing that the proposed architecture is able to achieve accurate results with fewer parameters, with a reduced risk of overfitting the training data.

中文翻译：

---

用于图像分析的混合尺度密集卷积神经网络

在最近的工作中，深度卷积神经网络已成功应用于许多图像处理问题。流行的网络体系结构通常会向标准体系结构添加其他操作和连接，以训练更深的网络。为了在实践中获得准确的结果，通常需要大量的可训练参数。在这里，我们介绍一种网络体系结构，该体系结构基于使用膨胀卷积来捕获不同图像比例的特征并将所有特征图彼此紧密地连接在一起。生成的体系结构能够使用相对较少的参数来获得准确的结果，并且由一组操作组成，从而使其更易于在实践中实施，训练和应用，并自动适应不同的问题。