Convolutional neural networks (CNNs) handle the case where filters extend beyond the image boundary using several heuristics, such as zero , repeat or mean padding. These schemes are applied in an ad-hoc fashion and, being weakly related to the image content and oblivious of the target task, result in low output quality at the boundary. In this paper, we propose a simple and effective improvement that learns the boundary handling itself. At training-time, the network is provided with a separate set of explicit boundary filters. At testing-time, we use these filters which have learned to extrapolate features at the boundary in an optimal way for the specific task. Our extensive evaluation, over a wide range of architectural changes (variations of layers, feature channels, or both), shows how the explicit filters result in improved boundary handling. Furthermore, we investigate the efficacy of variations of such boundary filters with respect to convergence speed and accuracy. Finally, we demonstrate an improvement of 5–20% across the board of typical CNN applications (colorization, de-Bayering, optical flow, disparity estimation, and super-resolution). Supplementary material and code can be downloaded from the project page: http://geometry.cs.ucl.ac.uk/projects/2019/investigating-edge/ .

中文翻译：

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边缘学习：研究 CNN 中的边界滤波器

卷积神经网络 (CNN) 使用几种启发式方法（例如零、重复或均值填充）处理过滤器超出图像边界的情况。这些方案以临时方式应用，并且与图像内容的相关性较弱并且不考虑目标任务，导致边界处的输出质量较低。在本文中，我们提出了一种简单有效的改进方法，可以学习边界处理本身。在训练时，网络提供了一组单独的显式边界过滤器。在测试时，我们使用这些过滤器，这些过滤器已经学会以最佳方式为特定任务推断边界处的特征。我们对广泛的架构变化（层的变化、特征通道或两者）进行了广泛的评估，展示了显式过滤器如何改善边界处理。此外，我们研究了这种边界滤波器在收敛速度和准确性方面的变化的功效。最后，我们展示了典型 CNN 应用（着色、去拜耳、光流、视差估计和超分辨率）的 5-20% 的改进。补充材料和代码可从项目页面下载：http://geometry.cs.ucl.ac.uk/projects/2019/investigating-edge/。