We propose a probabilistic framework for domain adaptation that blends both generative and discriminative modeling in a principled way. Under this framework, generative and discriminative models correspond to specific choices of the prior over parameters. This provides us a very general way to interpolate between generative and discriminative extremes through different choices of priors. By maximizing both the marginal and the conditional log-likelihoods, models derived from this framework can use both labeled instances from the source domain as well as unlabeled instances from both source and target domains. Under this framework, we show that the popular reconstruction loss of autoencoder corresponds to an upper bound of the negative marginal log-likelihoods of unlabeled instances, where marginal distributions are given by proper kernel density estimations. This provides a way to interpret the empirical success of autoencoders in domain adaptation and semi-supervised learning. We instantiate our framework using neural networks, and build a concrete model, DAuto. Empirically, we demonstrate the effectiveness of DAuto on text, image and speech datasets, showing that it outperforms related competitors when domain adaptation is possible.
The authors propose a probabilistic framework for semi-supervised learning and domain adaptation. By varying the prior distribution, the framework can incorporate both generative and discriminative modeling.  The authors emphasize on one particular form of constraint on the prior distribution, that is weight (parameter) sharing, and come up with a concrete model named Dauto for domain adaptation. A domain confusion loss is added to learn domain-invariant feature representations. The authors compared Dauto with several baseline methods on several datasets and showed improvement. 
The paper is well-organized and easy to follow. The probabilistic framework itself is quite straight-forward. The paper will be more interesting if the authors are able to extend the discussion on different forms of prior instead of the simple parameter sharing scheme. 
The proposed DAuto is essentially DANN+autoencoder.  The minimax loss employed in DANN and DAuto is known to be prone to degenerated gradient for the generator. It would be interesting to see if the additional auto-encoder part help address the issue. 
The experiments miss some of the more recent baseline in domain adaptation, such as Adversarial Discriminative Domain Adaptation (Tzeng, Eric, et al. 2017). 
It could be more meaningful to organize the pairs in table by target domain instead of source, for example, grouping 9->9, 8->9, 7->9 and 3->9 in the same block. DAuto does seem to offer more boost in domain pairs that are less similar.