  The task of Visual Question Answering (VQA) is known to be plagued by the
issue of VQA models exploiting biases within the dataset to make its final
prediction. Various previous ensemble based debiasing methods have been
proposed where an additional model is purposefully trained to be biased in
order to train a robust target model. However, these methods compute the bias
for a model simply from the label statistics of the training data or from
single modal branches. In this work, in order to better learn the bias a target
VQA model suffers from, we propose a generative method to train the bias model
directly from the target model, called GenB. In particular, GenB employs a
generative network to learn the bias in the target model through a combination
of the adversarial objective and knowledge distillation. We then debias our
target model with GenB as a bias model, and show through extensive experiments
the effects of our method on various VQA bias datasets including VQA-CP2,
VQA-CP1, GQA-OOD, and VQA-CE, and show state-of-the-art results with the LXMERT
architecture on VQA-CP2.
