Venue: ICLR 2020 Conference

Reviews:

final_score: rating: 1: Reject
review_content: This paper introduces Distribution Matching Prototypical Network (DMPN) for Unsupervised Domain Adaptation (UDA). The proposed method explicitly models the feature distribution as a Gaussian mixture model in both source and target domains. Then the method aligns the target distribution with the source distribution by minimizing losses, which are called Gaussian Component Mean Matching (GCMM) and Pseudo Distribution Matching (PDM).

This paper should be rejected because (1) the novelty of the main idea is marginal, and (2) the performance gain over the baseline methods is also marginal.

Pan et al. already proposed the idea of transferring the knowledge from the source to the target using the prototype of each class. It is required to explain why explicit modeling performs better than implicit modeling of prototypes by theory or practice.

In table 2, the proposed method seems better than TPN, but in the appendix, by comparing then in each category, the proposed method wins six categories, whereas TPN also wins six categories. Therefore, it is hard to say the proposed DMPN is more effective than another method.

Each prototype is modeled using a mean and a covariance matrix. Why the authors don't use the estimated covariance matrix to measure the distance in eq.5?

Because the proposed method uses pseudo-labeling for the target domain, it seems that the weights to determine unreliable examples are crucial. The paper should show the sensitivity of ways to determine the weights. What happens if values of 0.1 and 0.9 are changed in (pi-0.1)/0.9 on page 6?
review_title: Official Blind Review #2

final_score: rating: 3: Weak Reject
review_content: The paper develops a new method for adapting models trained on labeled data from some source domain to unlabeled data in a target domain. The authors accomplish this by adapting a technique from [1] and [2] enforcing that the deep features learned during training approximately follow a Gaussian mixture distribution. With the learned features in this form, the authors ensure domain adaptation by minimizing the discrepancy between the distributions arising from the source and target datasets.

Strengths:
 + The paper's experiments show an improvement in the model's performance relative to past work, utilizing a large number of comparison models.
 + The use of explicit distributional information within the learned representations seems like a good fit for the task at hand, and the authors' experiments back this up.

Weaknesses:
 - The proposed method for unsupervised domain adaptation is very similar to the prototypical networks approach in [3], with the primary difference being a loss term incentivizing a Gaussian mixture distribution over features.
 - While the authors achieve improved performance over [3], the gains in classification accuracy on the target dataset aren't especially huge (~1-3%).
 - The paper is a bit hard to follow, and would be improved by giving a more explicit comparison of the methods used here to past work, especially [1] and [3].


[1] Weitao Wan, Yuanyi Zhong, Tianpeng Li, and Jiansheng Chen. Rethinking feature distribution for loss functions in image classification. 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 9117–9126, 2018.

[2] Hong-Ming Yang, Xu-Yao Zhang, Fangying Yin, and Chenglin Liu. Robust classification with convolutional prototype learning. 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 3474–3482, 2018.

[3] Yingwei Pan, Ting Yao, Yehao Li, Yu Wang, Chong-Wah Ngo, and Tao Mei. Transferrable prototypical networks for unsupervised domain adaptation. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2239–2247, 2019.
review_title: Official Blind Review #1

final_score: rating: 3: Weak Reject
review_content: <Paper summary>
The authors proposed Distribution Matching Prototypical Network (DMPN) for unsupervised domain adaptation. DMPN extracts features from the input data and models them as Gaussian mixture distributions. By explicitly modeling the distributions that the features follow, the discrepancy between the distribution of source data and that of target data can be easily evaluated. DMPN is trained by jointly minimizing two kinds of loss, which are classification loss on the source data and domain discrepancy loss that is calculated via the explicit models. Experimental results on two popular benchmark datasets validate the advantage of DMPN over other state-of-the-art methods. 

<Review summary>
The proposed method seems simple but empirically performs well. The paper is well written and easy to follow, so we can maybe easily implement it. However, I have several concerns mainly about the details and theories of the proposed method, which makes my score a bit lower than the border line. Given clarifications in an author response, I would be willing to increase the score.

<Details>
* Strength
 + The motivation of using ProtoNet for domain adaptation seems reasonable.
 + The proposed method performs well in the experiments.
 + The paper, especially the experiment section, is well written and easy to follow.


* Weakness and concerns
 - Several points on the proposed loss (GCMM and PDM) are not sufficiently discussed.
  -- Why do we need two kinds of loss? These losses seem to play almost same role. Since PDM loss corresponds to target-side log likelihood regularization term (Eq. (3)), I wonder if we really need GCMM loss. 
  -- Since the authors explicitly model the feature distributions by Gaussian mixtures (GMs), it might be possible to calculate a standard divergence between source and target data distributions by using the parameters of GMs. Compared with such a straightforward approach, the proposed method seems to be ad-hoc and is not theoretically validated. What term of divergence (or distance) does it minimize?
  -- When a certain class does not appear in pseudo-labeled target data, how can we calculate GCMM loss? (specifically, \mu^{et}_c)
  -- Are Eq. (3) and Eq. (6) correct? These are defined as total loss, not average, over each domain. It means that the scale of the coefficients for these terms changes according to the number of training data, but the sensitivity analysis in Fig. 2 does not show such effect.
  -- Since the proposed losses heavily depend on the pseudo labels on the target data, it should be important to carefully set a proper threshold for the confidence. Is the proposed method sensitive against the change of this threshold? If so, how can we tune it?
  -- How can we know p(c) in advance?

 - The theory shown in 3.5 is not sufficiently validated. 
  -- The authors state ````we minimize the first term through minimizing the domain discrepancy losses," but it is not sufficiently supported, because the relationship between the proposed losses and H-delta-H divergence is not clear. 

 - I am concerned about whether the proposed method works well with harder datasets such as Office-Home dataset, because each class data are modeled by a simple Gaussian distribution in the proposed method. 


* Minor concerns that do not have an impact on the score
 - Using both f^s_i and F(x^s_i; \theta) is confusing.
 - Typo in Eq. (7): PMD -> PDM

review_title: Official Blind Review #3

Meta-review: This paper addresses the problem of unsupervised domain adaptation and proposes explicit modeling of the source and target feature distributions to aid in cross-domain alignment. 

The reviewers all recommended rejection of this work. Though they all understood the paper’s position of explicit feature distribution modeling, there was a lack of understanding as to why this explicit modeling should be superior to the common implicit modeling done in related literature. As some reviewers raised concern that the empirical performance of the proposed approach was marginally better than competing methods, this experimental evidence alone was not sufficient justification of the explicit modeling. There was also a secondary concern about whether the two proposed loss functions were simultaneously necessary. 

Overall, after reading the reviewers and authors comments, the AC recommends this paper not be accepted. 
Decision: Reject