Signal Is Harder To Learn Than Bias: Debiasing with Focal Loss
Keywords: debiasing, focal loss, representation learning, variational autoencoders, deep generative models, interpretability
TL;DR: We propose a VAE-based model that learns debiased representations using a disentangling reweighting scheme inspired by the focal loss
Abstract: Spurious correlations are everywhere. While humans often do not perceive them, neural networks are notorious for learning unwanted associations, also known as biases, instead of the underlying decision rule. As a result, practitioners are often unaware of the biased decision-making of their classifiers. Such a biased model based on spurious correlations might not generalize to unobserved data, leading to unintended, adverse consequences.
We propose Signal is Harder (SiH), a variational-autoencoder-based method that simultaneously trains a biased and unbiased classifier using a novel, disentangling reweighting scheme inspired by the focal loss. Using the unbiased classifier, SiH matches or improves upon the performance of state-of-the-art debiasing methods. To improve the interpretability of our technique, we propose a perturbation scheme in the latent space for visualizing the bias that helps practitioners become aware of the sources of spurious correlations.
Submission Number: 21
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