  Recent studies show that advanced priors play a major role in deep generative
models. Exemplar VAE, as a variant of VAE with an exemplar-based prior, has
achieved impressive results. However, due to the nature of model design, an
exemplar-based model usually requires vast amounts of data to participate in
training, which leads to huge computational complexity. To address this issue,
we propose Bayesian Pseudocoresets Exemplar VAE (ByPE-VAE), a new variant of
VAE with a prior based on Bayesian pseudocoreset. The proposed prior is
conditioned on a small-scale pseudocoreset rather than the whole dataset for
reducing the computational cost and avoiding overfitting. Simultaneously, we
obtain the optimal pseudocoreset via a stochastic optimization algorithm during
VAE training aiming to minimize the Kullback-Leibler divergence between the
prior based on the pseudocoreset and that based on the whole dataset.
Experimental results show that ByPE-VAE can achieve competitive improvements
over the state-of-the-art VAEs in the tasks of density estimation,
representation learning, and generative data augmentation. Particularly, on a
basic VAE architecture, ByPE-VAE is up to 3 times faster than Exemplar VAE
while almost holding the performance. Code is available at
\url{https://github.com/Aiqz/ByPE-VAE}.

Machine Learning