Don’t Take the Easy Way Out:Ensemble Based Methods for Avoiding Known Dataset Biases
Table 3: Results on the VQA-CP v2.0 test set.
['Debiasing Method', 'Acc.']
Main Model: This model uses a multi-label objective, so we apply our ensemble methods by treating each possible answer as a two-class classification problem. The learned-mixin method was highly effective, boosting performance on VQA-CP by about 9 points, and the entropy regularizer can increase this by another 3 points, significantly surpassing prior work. For the learned-mixin ensemble, we find g(xi) is strongly correlated with the bias’s expected when it knows if can rely on the bias-only model.