Reducing Bias in Pre-trained Models by Tuning while Penalizing Change
Niklas Penzel Gideon Stein Joachim Denzler
Deep models trained on large amounts of data often incorporate implicit
biases present during training time. If later such a bias is discovered during
inference or deployment, it is often necessary to acquire new data and retrain
the model. This behavior is especially problematic in critical areas such as
autonomous driving or medical decision-making. In these scenarios, new data is
often expensive and hard to come by. In this work, we present a method based on
change penalization that takes a pre-trained model and adapts the weights to
mitigate a previously detected bias. We achieve this by tuning a
zero-initialized copy of a frozen pre-trained network. Our method needs very
few, in extreme cases only a single, examples that contradict the bias to
increase performance. Additionally, we propose an early stopping criterion to
modify baselines and reduce overfitting. We evaluate our approach on a
well-known bias in skin lesion classification and three other datasets from the
domain shift literature. We find that our approach works especially well with
very few images. Simple fine-tuning combined with our early stopping also leads
to performance benefits for a larger number of tuning samples.