In this paper, we introduce a collaborative training algorithm of balanced random forests for domain adaptation tasks which can avoid the overfitting problem. In real scenarios, most domain adaptation algorithms face the challenges from noisy, insufficient training data. Moreover in open set categorization, unknown or misaligned source and target categories adds difficulty. In such cases, conventional methods suffer from overfitting and fail to successfully transfer the knowledge of the source to the target domain. To address these issues, the following two techniques are proposed. First, we introduce the optimized decision tree construction method, in which the data at each node are split into equal sizes while maximizing the information gain. Compared to the conventional random forests, it generates larger and more balanced decision trees due to the even-split constraint, which contributes to enhanced discrimination power and reduced overfitting. Second, to tackle the domain misalignment problem, we propose the domain alignment loss which penalizes uneven splits of the source and target domain data. By collaboratively optimizing the information gain of the labeled source data as well as the entropy of unlabeled target data distributions, the proposed CoBRF algorithm achieves significantly better performance than the state-of-the-art methods. The proposed algorithm is extensively evaluated in various experimental setups in challenging domain adaptation tasks with noisy and small training data as well as open set domain adaptation problems, for two backbone networks of AlexNet and ResNet-50.
Summary:
This paper introduces a method for domain adaptation, where each domain has noisy examples. Their method is based on a decision tree in which the data at each node are split into equal sizes while maximizing the
information gain.  They also proposed a way to reduce domain alignment. Their method is tested on several noisy domain adaptation settings and performs better than other baseline methods. 
Pros:
Their idea to utilize a decision tree for domain adaptation sounds novel. 
Experiments indicate the effectiveness of their method.
Cons:
This paper is not well-written and has many unclear parts. 
1, The presentation of the problem set is unclear throughout this paper. In the abstract, they mentioned that they tackle the situation where both source and target domains contain noisy examples. However, they did not define the exact problem setting in any section. I could not understand what kind of problem setting motivated their method, which makes it hard to understand their method. 
2, How they actually optimized the model is also unclear. From Eq 1~4, it is hard to grasp how they trained the model. 
3, In open-set domain adaptation, simply minimizing domain-distance can harm the performance. How does the method avoid this issue? It was also unclear. 
4, Experimental setting seems to be wrong and unclear. In Openset1, they say that "The labels from 1 to 10 of both source and target domains are marked as the known class, and all data with label 11∼20 in the source domain and label 21∼31 in the
target domain are used as one unknown class". However, Saito et al. (2018) used 21-31 classes in the target domain as one unknown class. In addition, "According to Saito et al. (2018) the target data of the unknown class is not used in training, ", they used the 21-31 classes for training in an unsupervised way. How is this method used to detect unknown class? Is there any threshold value set for it?
5, The experimental setting is unclear. In 4.4, ", we use only 10% of training samples", does it mean 10 % training source examples or target examples? This setting is also unclear. 
From the cons written above, this paper has too many unclear parts in the experiments and method section. I cannot say the result is reproducible given the content of the paper and the result is a reliable one. They need to present more carefully designed experiments.