  The problem of catastrophic forgetting occurs in deep learning models trained
on multiple databases in a sequential manner. Recently, generative replay
mechanisms (GRM), have been proposed to reproduce previously learned knowledge
aiming to reduce the forgetting. However, such approaches lack an appropriate
inference model and therefore can not provide latent representations of data.
In this paper, we propose a novel lifelong learning approach, namely the
Lifelong VAEGAN (L-VAEGAN), which not only induces a powerful generative replay
network but also learns meaningful latent representations, benefiting
representation learning. L-VAEGAN can allow to automatically embed the
information associated with different domains into several clusters in the
latent space, while also capturing semantically meaningful shared latent
variables, across different data domains. The proposed model supports many
downstream tasks that traditional generative replay methods can not, including
interpolation and inference across different data domains.

Computer Vision and Pattern Recognition;Machine Learning