Weakness: 1. This work raises an essential issue in partial domain adaptation and evaluates many PDA algorithms and model selection strategies. However, it does not present any solution to this problem. 2. The findings of the experiments are a bit trivial. No target label for model selection strategies will hurt the performance, and the random seed would influence the performance. They are commonsense in domain adaptation, even in deep learning. 3. The writing needs to improve. The Tables are referenced but always placed on different pages. For example, Table 2 is referred in page 4 but placed on page 3, making it hard to read. The paper also has many typos, e.g., ‘that’ instead of ‘than’ in section 3. 4. Many abbreviations lack definition and cause confusion. ‘AR’ in Table 5 stands for domain adaptation tasks and algorithms. 5. In section 4.2, heuristic strategies for Hyper-parameter turning is not clearly described. And the author said, “we only consider the model at the end of training”, but should we use the model selection strategies? 6. In section 5.2, part of Model Selection Strategies, the authors give a conclusion that seems to be wrong “only the JUMBOT and SND pair performed reasonably well with respect to the JUMBOT and ORACLE pair on both datasets.” In Table 6, the JUMBOT and SND pair performs worse than the JUMBOT and ORACLE pair by a large margin. For instance, on OFFICE-HOME, the JUMBOT and SND pair reaches 72.29 accuracies, while the JUMBOT and ORACLE pair achieves 77.15 accuracies.
4. Many abbreviations lack definition and cause confusion. ‘AR’ in Table 5 stands for domain adaptation tasks and algorithms.