Venue: ICLR 2017 workshop

Reviews:

final_score: rating: 6: Marginally above acceptance threshold
review_content: This paper has two parts: in the first, the authors try to unify a large subset of existing domain adaptation techniques into a figure that is claimed to shed more light into how they are similar and how they are different. The powerset of domain adaptation models is roughly described by the base model, weight sharing mechanism and the adversarial loss used (one note, I think CoGANs do actually tie some weights, no?). 

The authors note that not all possible combinations have been used and thus try a discriminative model that uses the GAN loss and unshared weights. All in all, this is a reasonable thing to try (though there are clearly other missing combinations!). I'm surprised that the authors chose to fix the source model during training -- curious if that makes a practical difference. The Office results look decent (as much as we can glean from Office results to be honest). 

NB: I only read the workshop submission, not the full tech report cited within.
review_title: A small new spin on adversarial domain adaptation

final_score: rating: 7: Good paper, accept
review_content: This paper proposed an interesting application of the adversarial networks in the context of domain adaptation. It gives a unified view of the existing domain transfer work, and provides a neat solution to integrate adversarial training to the DA application. I am not sure about the novelty of the approach as it is a combination of well known technology, but the idea is original.
review_title: Interesting paper as a novel use case for adversarial training

Decision: Accept