  Many machine learning algorithms are trained and evaluated by splitting data
from a single source into training and test sets. While such focus on
in-distribution learning scenarios has led to interesting advancement, it has
not been able to tell if models are relying on dataset biases as shortcuts for
successful prediction (e.g., using snow cues for recognising snowmobiles),
resulting in biased models that fail to generalise when the bias shifts to a
different class. The cross-bias generalisation problem has been addressed by
de-biasing training data through augmentation or re-sampling, which are often
prohibitive due to the data collection cost (e.g., collecting images of a
snowmobile on a desert) and the difficulty of quantifying or expressing biases
in the first place. In this work, we propose a novel framework to train a
de-biased representation by encouraging it to be different from a set of
representations that are biased by design. This tactic is feasible in many
scenarios where it is much easier to define a set of biased representations
than to define and quantify bias. We demonstrate the efficacy of our method
across a variety of synthetic and real-world biases; our experiments show that
the method discourages models from taking bias shortcuts, resulting in improved
generalisation. Source code is available at https://github.com/clovaai/rebias.
