  Image classification models tend to make decisions based on peripheral
attributes of data items that have strong correlation with a target variable
(i.e., dataset bias). These biased models suffer from the poor generalization
capability when evaluated on unbiased datasets. Existing approaches for
debiasing often identify and emphasize those samples with no such correlation
(i.e., bias-conflicting) without defining the bias type in advance. However,
such bias-conflicting samples are significantly scarce in biased datasets,
limiting the debiasing capability of these approaches. This paper first
presents an empirical analysis revealing that training with "diverse"
bias-conflicting samples beyond a given training set is crucial for debiasing
as well as the generalization capability. Based on this observation, we propose
a novel feature-level data augmentation technique in order to synthesize
diverse bias-conflicting samples. To this end, our method learns the
disentangled representation of (1) the intrinsic attributes (i.e., those
inherently defining a certain class) and (2) bias attributes (i.e., peripheral
attributes causing the bias), from a large number of bias-aligned samples, the
bias attributes of which have strong correlation with the target variable.
Using the disentangled representation, we synthesize bias-conflicting samples
that contain the diverse intrinsic attributes of bias-aligned samples by
swapping their latent features. By utilizing these diversified bias-conflicting
features during the training, our approach achieves superior classification
accuracy and debiasing results against the existing baselines on synthetic and
real-world datasets.
