  Generative models often incur the catastrophic forgetting problem when they
are used to sequentially learning multiple tasks, i.e., lifelong generative
learning. Although there are some endeavors to tackle this problem, they suffer
from high time-consumptions or error accumulation. In this work, we develop an
efficient and effective lifelong generative model based on variational
autoencoder (VAE). Unlike the generative adversarial network, VAE enjoys high
efficiency in the training process, providing natural benefits with few
resources. We deduce a lifelong generative model by expending the intrinsic
reconstruction character of VAE to the historical knowledge retention. Further,
we devise a feedback strategy about the reconstructed data to alleviate the
error accumulation. Experiments on the lifelong generating tasks of MNIST,
FashionMNIST, and SVHN verified the efficacy of our approach, where the results
were comparable to SOTA.
