Paper:
Partial multi-label learning (PML), which tackles the problem of learning multilabel prediction models from instances with overcomplete noisy annotations, has recently started gaining attention from the research community. In this paper, we propose a novel adversarial learning model, PML-GAN, under a generalized encoder-decoder framework for partial multi-label learning. The PML-GAN model uses a disambiguation network to identify noisy labels and uses a multi-label prediction network to map the training instances to the disambiguated label vectors, while deploying a generative adversarial network as an inverse mapping from label vectors to data samples in the input feature space. The learning of the overall model corresponds to a minimax adversarial game, which enhances the correspondence of input features with the output labels. Extensive experiments are conducted on multiple datasets, while the proposed model demonstrates the state-of-the-art performance for partial multi-label learning.

Review:
This paper proposed a new method for partial multi-label learning, based on the idea of generative adversarial networks. The partial multi-label learning is the problem that one instance is associated with several ground truth labels simultaneously, but we are given a superset of the ground truth labels for training. To solve the problem, the paper proposes a learning model composing of four deep neural networks, two of them for prediction the ground truth labels (the prediction network, and the disambiguation network), one of them to generate instances based on the disambiguated labels, and one discriminative network to discriminated generated instance versus true instances. The four network are concatenated, and the paper proposed to optimize the sum of generation loss, the classification loss, and the adversarial loss. Theoretical results like the theories in the original GAN paper are also given. Finally, the paper does thorough empirical studies compared the proposed method with four other baselines and three variants on fourteen datasets with various settings.  
The paper is the first paper to proposes a GAN-style algorithm for the partial multi-label learning problem. The empirical results also validate its superior performance compared to other baselines on the data sets used. On the other hand, the paper fails to give enough intuition on why the GAN can naturally be used to solve the partial multi-label learning problem, and why the proposed method can perform better than other baselines. There is also some inaccuracy in discussing related work. Overall, given the contribution to introduce GAN to partial multi-label learning problem and the thorough empirical studies, I think this paper can be weakly accepted. 
Main arguments
The paper argues that existing methods may not be good on this problem is due to the fact that they generally learn a “confidence score” for every candidate labels, while the learned “confidence score” may not be accurate enough, and the errors may accumulate through these process. I agree with the paper on this aspect. However, in the paper, it also learns something similar to the “confidence” score in the disambiguation network \tilde D, so why the proposed method is better than previous baselines? I guess the reason is due to the adversarial training part in the latter steps. But the paper fails to give clear intuition why the adversarial training part can lead to better performance.
I am also wondering will the proposed model leads to a trivial solution. For example, one possible solution may be that both the prediction network F and the disambiguation network \tilde D get the same solution: the given partial label set. In this way, the classification error can always be zero, and we can train the generation network to give a ground truth instance x, given that the generation network G is strong enough. So I also guess the discrimination network is the key part for the success of the proposed method empirically. So I am curious if we only use the adversarial loss without training the prediction network F, what the results will be. In the Ablation Study Sec. 5.3, the paper does not compare the results optimizing only the adversarial loss. I believe the results of such a study can help us understand what the key part of the proposed method is.
In the related work part, the arguments on “weak label learning” cannot be used for partial multi-label learning is not accurate. In fact, “weak label learning” studies the problem when positive labels are missing, and partial multi-label learning studies the problem when negative labels are missing. So in general, the methods for “weak label learning” can be used for “partial multi-label learning” if we exchange the role of positive labels and negative labels in both problems. So why we need to study partial multi-label learning? I think the reason is that, generally, in multi-label learning, the number of negative labels will be much larger than the number of positive labels. So “partial multi-label” is actually more strong supervision information than “weak labels” because positive labels are more important. Such arguments and related empirical studies (when the number of negative labels is much larger than positive labels) are suggested to be added into the further version of the paper.
Although the paper has some deficiencies, it does a good job of introducing GAN into partial multi-label learning, and it is also the first paper to use the powerful deep neural networks into this problem, which may trigger some interesting studies given the booming of neural networks these days. And thorough empirical studies are done by comparing to not only baselines but also different loss parts of the proposal it owes. It is worth reading especially it may have an impact on further studies. 
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I am satisfied with the rebuttal and tend to increase my score. Although the paper is not perfect, it does a good job of introducing GAN into partial multi-label learning, and it is also the first paper (thus it is novel) to use the powerful deep neural networks into this problem, which may trigger some interesting studies given the booming of neural networks these days.
Actually, I think partial multi-label learning problem is difficult and it may not possible to have perfect solution nowadays. Partial multi-class learning may be easier since you know only one label is true, but partial multi-label learning is difficult since you do not know how many true labels there are. It makes no point to require a perfect solution for such a dirty problem.