Impact of Training Set Size on the Ability of Deep Neural Networks to Deal with Omission Noise

Abstract

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Deep Learning usually requires large amounts of labeled training data. In remote sensing, deep learning is often applied for land cover and land use classification as well as street network and building segmentation. In case of the latter, a common way of obtaining training labels is to leverage crowdsourced datasets which can provide numerous types of spatial information on a global scale. However, labels from crowdsourced datasets are often limited in the sense that they potentially contain high levels of noise. Understanding how such noisy labels impede the predictive performance of Deep Neural Networks (DNNs) is crucial for evaluating if crowdsourced data can be an answer to the need for large training sets by DNNs. One way towards this understanding is to identify the factors which affect the relationship between label noise and predictive performance of a model. The size of the training set could be one of these factors since it is well known for being able to greatly influence a model’s predictive performance. In this work we pick the size of the training set and study its influence on the robustness of a model against a common type of label noise known as omission noise. To this end, we utilize a dataset of aerial images for building segmentation and create several versions of the training labels by introducing different amounts of omission noise. We then train a state-of-the-art model on subsets of varying size of those versions. Our results show that the training set size does play a role in affecting the robustness of our model against label noise: A large training set improves the robustness of our model against omission noise.

Keywords

• deep learning
• remote sensing
• label noise
• robustness
• segmentation
• building segmentation