  Effective inference for a generative adversarial model remains an important
and challenging problem. We propose a novel approach, Decomposed Adversarial
Learned Inference (DALI), which explicitly matches prior and conditional
distributions in both data and code spaces, and puts a direct constraint on the
dependency structure of the generative model. We derive an equivalent form of
the prior and conditional matching objective that can be optimized efficiently
without any parametric assumption on the data. We validate the effectiveness of
DALI on the MNIST, CIFAR-10, and CelebA datasets by conducting quantitative and
qualitative evaluations. Results demonstrate that DALI significantly improves
both reconstruction and generation as compared to other adversarial inference
models.
