  Modern machine learning suffers from catastrophic forgetting when learning
new classes incrementally. The performance dramatically degrades due to the
missing data of old classes. Incremental learning methods have been proposed to
retain the knowledge acquired from the old classes, by using knowledge
distilling and keeping a few exemplars from the old classes. However, these
methods struggle to scale up to a large number of classes. We believe this is
because of the combination of two factors: (a) the data imbalance between the
old and new classes, and (b) the increasing number of visually similar classes.
Distinguishing between an increasing number of visually similar classes is
particularly challenging, when the training data is unbalanced. We propose a
simple and effective method to address this data imbalance issue. We found that
the last fully connected layer has a strong bias towards the new classes, and
this bias can be corrected by a linear model. With two bias parameters, our
method performs remarkably well on two large datasets: ImageNet (1000 classes)
and MS-Celeb-1M (10000 classes), outperforming the state-of-the-art algorithms
by 11.1% and 13.2% respectively.
