  Deep learning models suffer from catastrophic forgetting of the classes in
the older phases as they get trained on the classes introduced in the new phase
in the class-incremental learning setting. In this work, we show that the
effect of catastrophic forgetting on the model prediction varies with the
change in orientation of the same image, which is a novel finding. Based on
this, we propose a novel data-ensemble approach that combines the predictions
for the different orientations of the image to help the model retain further
information regarding the previously seen classes and thereby reduce the effect
of forgetting on the model predictions. However, we cannot directly use the
data-ensemble approach if the model is trained using traditional techniques.
Therefore, we also propose a novel dual-incremental learning framework that
involves jointly training the network with two incremental learning objectives,
i.e., the class-incremental learning objective and our proposed
data-incremental learning objective. In the dual-incremental learning
framework, each image belongs to two classes, i.e., the image class (for
class-incremental learning) and the orientation class (for data-incremental
learning). In class-incremental learning, each new phase introduces a new set
of classes, and the model cannot access the complete training data from the
older phases. In our proposed data-incremental learning, the orientation
classes remain the same across all the phases, and the data introduced by the
new phase in class-incremental learning acts as new training data for these
orientation classes. We empirically demonstrate that the dual-incremental
learning framework is vital to the data-ensemble approach. We apply our
proposed approach to state-of-the-art class-incremental learning methods and
empirically show that our framework significantly improves the performance of
these methods.
