Biased Binary Attribute Classifiers Ignore the Majority Classes
Xinyi Zhang Johanna Sophie Bieri Manuel Günther
To visualize the regions of interest that classifiers base their decisions
on, different Class Activation Mapping (CAM) methods have been developed.
However, all of these techniques target categorical classifiers only, though
most real-world tasks are binary classification. In this paper, we extend
gradient-based CAM techniques to work with binary classifiers and visualize the
active regions for binary facial attribute classifiers. When training an
unbalanced binary classifier on an imbalanced dataset, it is well-known that
the majority class, i.e. the class with many training samples, is mostly
predicted much better than minority class with few training instances. In our
experiments on the CelebA dataset, we verify these results, when training an
unbalanced classifier to extract 40 facial attributes simultaneously. One would
expect that the biased classifier has learned to extract features mainly for
the majority classes and that the proportional energy of the activations mainly
reside in certain specific regions of the image where the attribute is located.
However, we find very little regular activation for samples of majority
classes, while the active regions for minority classes seem mostly reasonable
and overlap with our expectations. These results suggest that biased
classifiers mainly rely on bias activation for majority classes. When training
a balanced classifier on the imbalanced data by employing attribute-specific
class weights, majority and minority classes are classified similarly well and
show expected activations for almost all attributes