A debiasing framework for deep learning applied to the morphological classification of galaxies

The morphologies of galaxies and their relation with physical features have been extensively studied in the past. Galaxy morphology labels are usually created by humans and are used to train machine learning models. Human labels have been shown to contain biases in terms of observational parameters such as the resolution of the labeled images. In this work, we demonstrate that deep learning models trained on biased galaxy data produce biased predictions. We also propose a method to train neural networks that takes into account this inherent labeling bias. We show that our deep de-biasing method is able to reduce the bias of the models even when trained using biased data.