We tackle unsupervised domain adaptation by accounting for the fact that different domains may need to be processed differently to arrive to a common feature representation effective for recognition. To this end, we introduce a deep learning framework where each domain undergoes a different sequence of operations, allowing some, possibly more complex, domains to go through more computations than others. This contrasts with state-of-the-art domain adaptation techniques that force all domains to be processed with the same series of operations, even when using multi-stream architectures whose parameters are not shared. As evidenced by our experiments, the greater flexibility of our method translates to higher accuracy. Furthermore, it allows us to handle any number of domains simultaneously.
In this paper, the authors proposed to address the information asymmetry between domains in unsupervised domain adaptation. Innovatively, they resort to a multiflow network where each domain adaptatively selects its own pipeline. I quite appreciate the idea itself, while there are many essential issues to be addressed first. 
Pros:
-	The way tackling the information asymmetry, or untie weights, between domains is novel and interesting. 
-	The proposed network/framework can be easily extended to the multi-task setting, or multi-source/multi-target domain adaptation.
-	The paper is well-written and easy to follow. 
Cons:
-	The most critical downside of this paper is its insufficient experiments to support the whole idea, where we will detail in the next.
Experimental issues:
-	Comparison with other state-of-the-art UDA methods (e.g., CDAN) is a must. This paper improves UDA in terms of adaptive parameters sharing, which is completely independent from most of the UDA contributions (including the DANN you compared) which improve the distribution alignment between feature representations. Therefore, it is imperative to compare that line of SOTA methods, otherwise why should we consider adaptative parameters sharing instead of distribution alignment? At best, the proposed multiflow network combined with the SOTA feature alignment method (e.g., CDAN other than DANN) should be considered and expected to beat CDAN itself. 
-	Many ablation studies or hyperparameter sensitivity analyses are missing. 
o	How do you determine the number of parallel flows, i.e., K? Is it possible that 3 or 4, more than 2 flows, are better even in the UDA between two domains? 
o	Do you try any other possibilities of grouping a computational unit, and how will different configurations influence the performance? 
o	Is there a possibility that none of the gates in the final layer is activated? Do you need some constraints?
-	Since the authors mentioned the potential of the multi-flow network in adaptation between multiple domains, it is necessary to investigate multi-source or multi-target domain adaptation. Only in this case may the significance of different K values be demonstrated. 
-	The baseline results in Table 1 are not comparable to some reported papers, and even lower than those reported in other UDA papers.