  Class-incremental learning of deep networks sequentially increases the number
of classes to be classified. During training, the network has only access to
data of one task at a time, where each task contains several classes. In this
setting, networks suffer from catastrophic forgetting which refers to the
drastic drop in performance on previous tasks. The vast majority of methods
have studied this scenario for classification networks, where for each new task
the classification layer of the network must be augmented with additional
weights to make room for the newly added classes. Embedding networks have the
advantage that new classes can be naturally included into the network without
adding new weights. Therefore, we study incremental learning for embedding
networks. In addition, we propose a new method to estimate the drift, called
semantic drift, of features and compensate for it without the need of any
exemplars. We approximate the drift of previous tasks based on the drift that
is experienced by current task data. We perform experiments on fine-grained
datasets, CIFAR100 and ImageNet-Subset. We demonstrate that embedding networks
suffer significantly less from catastrophic forgetting. We outperform existing
methods which do not require exemplars and obtain competitive results compared
to methods which store exemplars. Furthermore, we show that our proposed SDC
when combined with existing methods to prevent forgetting consistently improves
results.
