  Large scale datasets created from crowdsourced 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 original ideas that help us to
leverage such datasets in the context of change detection. First, we propose
the guided anisotropic diffusion (GAD) algorithm, which improves semantic
segmentation results using the input images as guides to perform edge
preserving filtering. We then show its potential in two weakly-supervised
learning strategies tailored for change detection. The first strategy is an
iterative learning method that combines model optimisation and data cleansing
using GAD to extract the useful information from a large scale change detection
dataset generated from open vector data. The second one incorporates GAD within
a novel spatial attention layer that increases the accuracy of weakly
supervised networks trained to perform pixel-level predictions from image-level
labels. Improvements with respect to state-of-the-art are demonstrated on 4
different public datasets.
