D-GAN: Divergent generative adversarial network for positive unlabeled learning and counter-examples generation
Positive Unlabeled (PU) learning consists in learning to distinguish samples of our class of interest, the positive class, from the counter-examples, the negative class, by using positive labeled and unlabeled samples during the training. Recent approaches exploit the GANs abilities to address the PU learning problem by generating relevant counter-examples. In this paper, we propose a new GAN-based PU learning approach named Divergent-GAN (D-GAN). The key idea is to incorporate a standard Positive Unlabeled learning risk inside the GAN discriminator loss function. In this way, the discriminator can ask the generator to converge towards the unlabeled samples distribution while diverging from the positive samples distribution. This enables the generator convergence towards the unlabeled counter-examples distribution without using prior knowledge, while keeping the standard adversarial GAN architecture. In addition, we discuss normalization techniques in the context of the proposed framework. Experimental results show that the proposed approach overcomes previous GAN-based PU learning methods issues, and it globally outperforms two-stage state of the art PU learning performances in terms of stability and prediction on both simple and complex image datasets.
 The motivation of the work is not clear but the novelty seems to be present. The paper is very hard to follow as the problem description and intuition of the D-GAN is not clearly written. Based on the experiments, the proposed method achieves marginal improvement in terms of F1 score but sometimes also slightly lower performance than other GAN based such as PGAN, so the impact of this work to solve positive unlabelled data problem is not evident. I am personally not as familiar with the PU problem and existing frameworks so my confidence in the assessment is low; my main experience is in the computer vision for autonomous driving and sparse coding. But my feeling is this paper is marginally below the threshold of acceptance.
You are a member of the scientific community tasked with peer review. Review the following paper title and abstract.
### Title + Abstract
D-GAN: Divergent generative adversarial network for positive unlabeled learning and counter-examples generation
Positive Unlabeled (PU) learning consists in learning to distinguish samples of our class of interest, the positive class, from the counter-examples, the negative class, by using positive labeled and unlabeled samples during the training. Recent approaches exploit the GANs abilities to address the PU learning problem by generating relevant counter-examples. In this paper, we propose a new GAN-based PU learning approach named Divergent-GAN (D-GAN). The key idea is to incorporate a standard Positive Unlabeled learning risk inside the GAN discriminator loss function. In this way, the discriminator can ask the generator to converge towards the unlabeled samples distribution while diverging from the positive samples distribution. This enables the generator convergence towards the unlabeled counter-examples distribution without using prior knowledge, while keeping the standard adversarial GAN architecture. In addition, we discuss normalization techniques in the context of the proposed framework. Experimental results show that the proposed approach overcomes previous GAN-based PU learning methods issues, and it globally outperforms two-stage state of the art PU learning performances in terms of stability and prediction on both simple and complex image datasets.
user
<think>Okay, to do a decent peer review, I should start with summarizing what the work is about. This includes novelty, contributions, clarity, and impact. I might include suggestions to issues that I find critical to the usefulness of the work. Starting with novelty, there are different types of novelty. The first aspect of novelty is scope and scale. Is the work covering a broader perspective than previous works? The second aspect is methodology. Is the proposed methodology novel compared to previous methods? Do they provide any significant advantage over previous methodologies? The third aspect is results. Are the results different compared previous work? Does this lead to any novel theories or an extension of current theories? Next is clarity, I will be reading the work in depth and analyze if any sentences can be potentially ambiguous or misleading. If any sentences are ambiguous, I will state them and give a suggestion on how to rephrase such sentence or how to make more clear to the audience. Lastly, impact is important. I will attempt to predict how such work will impact the scientific community. This might include the applications of such work and how the work might transform the way we study a certain topic. Finally, I will might only mention issues that are critical. This is to keep the review as concise and helpful as possible.</think>



 The motivation of the work is not clear but the novelty seems to be present. The paper is very hard to follow as the problem description and intuition of the D-GAN is not clearly written. Based on the experiments, the proposed method achieves marginal improvement in terms of F1 score but sometimes also slightly lower performance than other GAN based such as PGAN, so the impact of this work to solve positive unlabelled data problem is not evident. I am personally not as familiar with the PU problem and existing frameworks so my confidence in the assessment is low; my main experience is in the computer vision for autonomous driving and sparse coding. But my feeling is this paper is marginally below the threshold of acceptance.
assistant
 The motivation of the work is not clear but the novelty seems to be present. The paper is very hard to follow as the problem description and intuition of the D-GAN is not clearly written. Based on the experiments, the proposed method achieves marginal improvement in terms of F1 score but sometimes also slightly lower performance than other GAN based such as PGAN, so the impact of this work to solve positive unlabelled data problem is not evident. I am personally not as familiar with the PU problem and existing frameworks so my confidence in the assessment is low; my main experience is in the computer vision for autonomous driving and sparse coding. But my feeling is this paper is marginally below the threshold of acceptance.