of this paper include the following aspects: 1. This paper is not well written and some parts are hard to follow. It lacks necessary logical transition and important figures. For example, it lacks explanations to support the connection between the proposed training objective and the Cross Margin Discrepancy. Also, it should at least contain one figure to explain the overall architecture or training pipeline. 2. The authors claim that there is still no research focusing on the joint error for UDA. But this problem of arbitrarily increased joint error has already been studied in previous works like “Domain Adaptation with Asymmetrically-Relaxed Distribution Alignment”, in ICML2019. The authors should discuss on that work and directly illustrate the relationship between that work and the proposed one, and why the proposed method is better. 3. Although the joint error is indeed included in the proposed upper bound, in practice the authors have to use Source-driven Hypothesis Space and Target-driven Hypothesis Space to obtain approximation of f_{S} and f_{T}. To me, in practice the use of three classifiers h, f_{1}, f_{2} is just like an improvement over MCD. Hence, I doubt whether the proposed method can still simultaneously minimize the domain discrepancy and the joint error. For example, as shown in the Digit experiments, the performance is highly sensitive to the choice of \gamma in SHS, and sometimes the optimal \gamma value is conflicting for different domains in the same dataset, which is strange since according to the paper’s theorem, smaller \gamma only means more relaxed constraint on hypothesis space. Also, as shown in the VisDA experiments, the optimal value of \eta is close to 1, which means classification error from the approximate target domain is basically useless. 4. The benchmark results are inferior to the state-of-the-art methods. For instance, the Contrastive Adaptation Network achieves an average of 87.2 on VisDA2017, which is much higher than 79.7 achieved by the proposed method. And the same goes with Digit, Office31, and Office-Home dataset.
2. The authors claim that there is still no research focusing on the joint error for UDA. But this problem of arbitrarily increased joint error has already been studied in previous works like “Domain Adaptation with Asymmetrically-Relaxed Distribution Alignment”, in ICML2019. The authors should discuss on that work and directly illustrate the relationship between that work and the proposed one, and why the proposed method is better.