  Deep generative models (DGMs) have shown promise in image generation.
However, most of the existing work learn the model by simply optimizing a
divergence between the marginal distributions of the model and the data, and
often fail to capture the rich structures and relations in multi-object images.
Human knowledge is a critical element to the success of DGMs to infer these
structures. In this paper, we propose the amortized structural regularization
(ASR) framework, which adopts the posterior regularization (PR) to embed human
knowledge into DGMs via a set of structural constraints. We derive a lower
bound of the regularized log-likelihood, which can be jointly optimized with
respect to the generative model and recognition model efficiently. Empirical
results show that ASR significantly outperforms the DGM baselines in terms of
inference accuracy and sample quality.

Machine Learning;Machine Learning