The trustworthiness of f_p In spite of the clear assumption statements, I have concerns of utilizing f_p in the given setting. I am comfortable up to the derivation of Theorem 6 and Eq 6. However, the authors use Eq 7 to optimize the KL divergence, and Eq 7 uses the expectation with the distribution of f_p. While the paper asserts that the distribution function, f_p, can be approximated by the positive dataset. However, Algorithm 1 uses the sample minibatch of B P to empirically estimate f_p.

This paper presents an improved method for learning binary classifiers from positive and unlabeled data. Prior work has required the specification of the proportion of positive data in the unlabeled data set. This parameter is difficult to estimate and the resulting classifier is sensitive to it. While this paper is not the first to attempt to do away with the class prior estimation problem, this paper reports better empirical performance with theoretical results on consistency. As noted by all of the reviewers, the paper is very clearly written and helpfully provides a summary table comparing and contrasting prior work with the current work.

Originality: The main contribution of the paper is to propose a structured representation for video prediction models based on extracting keypoints from images. Models that extract keypoints from images had been proposed before, and here the authors propose an extension of those ideas to video. The paper also has experiments to empirically analyze this representation, which is often lacking in other video prediction papers, despite the fact that learning representations is one of the main motivations for video prediction. Clarity: The paper is well organized and clearly written. Quality and significance: The experiments are sound and properly assess some of the points made by the authors. I believe there are some issues/typos with the model formulation.

The paper proposes a new model for video prediction with a structured representation based on object keypoints. It is a novel approach and also experiment methodology is interesting and generalizable. Reviewers initially asked many questions and the rebuttal was convincing, at least for the majority of reviewers.

Summary and Contributions: Distribution Aligning Refinery of Pseudo-label (DARP) For semi-supervised learning (SSL), DARP is proposed to match the pseudo-labels with the underlying class distribution of the unlabeled data. The objective function is to minimize the KL divergence of the "aligned" pseudo-labels with the original pseudo-labels subject to the constraints that the "aligned" pseudo-labels are consistent with desired class/label distribution for the unlabeled data. To speed up the process, DARP uses a coordinate ascent algorithm for the Largrangian dual of the objective function. The evaluation was conducted with the CIFAR10 dataset with various artificially degrees of imbalance. DARP was used with a few existing algorithms for imbalanced SSL.

This paper proposes an approach to semi-supervised learning for imbalanced classes. It is indeed non-trivial to combine local/global/perturbation consistency-based semi-supervised methods and fully supervised methods for imbalanced classes---this paper may be the first work along this direction. The paper is quite general and can be applied on top of any pseudo-labeling-based semi-supervised methods. It first estimates the true class-prior probability and then updates/modifies the pseudo labels by pushing their class-prior probability with a constrained convex optimization. While in the beginning the reviewers had some concerns (mainly the clarity and too few datasets), the authors did a particularly good job in their rebuttal (showing that the class-prior probability can be estimated rather than must be given).

The paper presents a semi-supervised approach for object detection that extends the consistency regularization used for image classification [14] for object detection. Concretely, it proposes using consistency losses for both classification and localization, as well as a background elimination technique that alleviates the class imbalance inherent to object detection. They evaluate their approach with two types of detectors (single and two-stage) on PASCAL VOT 2007 with unlabeled data from VOT2012 and COCO. Pros: The approach is novel, as far as I know no previous work addresses semi-supervised learning with consistency regularization for object detection. The use of JS divergence over L2 distance is justified and shown experimentally.

This paper introduces a semi-supervised approach for object detection that extends the consistency regularization used for image classification for object detection. The proposed approach is novel and interesting. The evaluation part can be improved to make the comparison more convincing, as suggested by several reviewers.

The reviewers agree that this work is novel and interesting, and makes an interesting contribution to the literature. Please take the reviewer comments into account while preparing the camera-ready version.

Additional Feedback: Overall comment: Although I enjoyed reading the paper and it proposes novel ideas for PU learning research, I couldn't give a high score because: I feel it is hard to compare between methods in the experiments due to the usage of different models for proposed/baselines, some of the work in this paper (Sec. Other comments: The output of logistic classifiers will be between 0 and 1, and theoretically it should be an estimate of p(y x). Practically, the estimate of p(y x) can become quite noisy, or may overfit and lead to peaky hat{p}(y x) distributions, according to papers like "On Calibration of Modern Neural Networks" (ICML 2017). Assuming \hat{\sigma}(x) p_tr(y -1 x) seems to be a strong assumption, but does this cause any issues in the experiments? A minor suggestion is to investigate confidence-calibration, and see how much sensitive the final PU classifier is for worse calibration.