Large scale datasets created from user labels or openly available data have
become crucial to provide training data for large scale learning algorithms.
While these datasets are easier to acquire, the data are frequently noisy and
unreliable, which is motivating research on weakly supervised learning
techniques. In this paper we propose an iterative learning method that extracts
the useful information from a large scale change detection dataset generated
from open vector data to train a fully convolutional network which surpasses
the performance obtained by naive supervised learning. We also propose the
guided anisotropic diffusion algorithm, which improves semantic segmentation
results using the input images as guides to perform edge preserving filtering,
and is used in conjunction with the iterative training method to improve
results.

本文提出了一种迭代学习方法，通过从开放矢量数据生成的大规模 change detection 数据集中提取有用信息来训练全卷积网络，该方法在弱监督学习技术中具有出色的性能，并提出引导各向异性扩散算法，用于与迭代训练方法相结合以改进语义分割结果。