  Modern deep learning methods have achieved great success in machine learning
and computer vision fields by learning a set of pre-defined datasets. Howerver,
these methods perform unsatisfactorily when applied into real-world situations.
The reason of this phenomenon is that learning new tasks leads the trained
model quickly forget the knowledge of old tasks, which is referred to as
catastrophic forgetting. Current state-of-the-art incremental learning methods
tackle catastrophic forgetting problem in traditional classification networks
and ignore the problem existing in embedding networks, which are the basic
networks for image retrieval, face recognition, zero-shot learning, etc.
Different from traditional incremental classification networks, the semantic
gap between the embedding spaces of two adjacent tasks is the main challenge
for embedding networks under incremental learning setting. Thus, we propose a
novel class-incremental method for embedding network, named as zero-shot
translation class-incremental method (ZSTCI), which leverages zero-shot
translation to estimate and compensate the semantic gap without any exemplars.
Then, we try to learn a unified representation for two adjacent tasks in
sequential learning process, which captures the relationships of previous
classes and current classes precisely. In addition, ZSTCI can easily be
combined with existing regularization-based incremental learning methods to
further improve performance of embedding networks. We conduct extensive
experiments on CUB-200-2011 and CIFAR100, and the experiment results prove the
effectiveness of our method. The code of our method has been released.
