COMPOSITION AND DECOMPOSITION OF GANS
In this work, we propose a composition/decomposition framework for adversarially training generative models on composed data - data where each sample can be thought of as being constructed from a fixed number of components. In our framework, samples are generated by sampling components from component generators and feeding these components to a composition function which combines them into a “composed sample”. This compositional training approach improves the modularity, extensibility and interpretability of Generative Adversarial Networks (GANs) - providing a principled way to incrementally construct complex models out of simpler component models, and allowing for explicit “division of responsibility” between these components. Using this framework, we define a family of learning tasks and evaluate their feasibility on two datasets in two different data modalities (image and text). Lastly, we derive sufficient conditions such that these compositional generative models are identifiable. Our work provides a principled approach to building on pretrained generative models or for exploiting the compositional nature of data distributions to train extensible and interpretable models.
 - There have been works on this before in the GAN literature, they have not been even cited, let alone being compared to in the experiments. Seminal examples include Donahue et al., ICLR 2018 "Semantically decomposing the latent spaces of generative adversarial networks", and (a bit less starkly in terms of the alignment with the goals of this paper): Huang et al., 2017 "Stacked generative adversarial networks". - In general, comparisons to state-of-the-art (or to other) algorithms are missing. - Is the assumptions of pre-trained components viable with image, and not text, data? Please elaborate - The related work section is missing out on dozens of works, those on disentanglement or interpretability; what is the point then of making a related work section in the first place if only one single example of an algorithm in each broad topic is mentioned? If so, I would suggest mentioning this single example prior to the discussing the topic without a related work section, or (apparently the better option) to do a related work section with a rigorous coverage. Examples of some related works on disentanglement and interpretability: Higgins et al., ICLR 2017 "beta-VAE" - Kim & Mnih, ICML 2018 "Disentangling by factorising" - Adel et al., ICML 2018 "Discovering interpretable representations for both deep generative and discriminative models" - Chen et al., NIPS 2017 "InfoGAN: Interpretable representation learning by information maximizing generative adversarial nets", etc. - The advantages promised in Section 1 are a little bit too presumptuous. Too many idealistic assumptions are need in order for these advantages to hold. For instance, extensibility has been mentioned as an advantage in Section 1 and in the abstract, and that has not been capitalised on, or confirmed in the experiments, or from this point onwards. - It will be interesting to see what happens with rather real-world cases like occlusion, etc - Writing has room for improvements, in terms of both the flow and also grammar, etc. There are a few typos.
You are a member of the scientific community tasked with peer review. Review the following paper title and abstract.
### Title + Abstract
COMPOSITION AND DECOMPOSITION OF GANS
In this work, we propose a composition/decomposition framework for adversarially training generative models on composed data - data where each sample can be thought of as being constructed from a fixed number of components. In our framework, samples are generated by sampling components from component generators and feeding these components to a composition function which combines them into a “composed sample”. This compositional training approach improves the modularity, extensibility and interpretability of Generative Adversarial Networks (GANs) - providing a principled way to incrementally construct complex models out of simpler component models, and allowing for explicit “division of responsibility” between these components. Using this framework, we define a family of learning tasks and evaluate their feasibility on two datasets in two different data modalities (image and text). Lastly, we derive sufficient conditions such that these compositional generative models are identifiable. Our work provides a principled approach to building on pretrained generative models or for exploiting the compositional nature of data distributions to train extensible and interpretable models.
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>



 - There have been works on this before in the GAN literature, they have not been even cited, let alone being compared to in the experiments. Seminal examples include Donahue et al., ICLR 2018 "Semantically decomposing the latent spaces of generative adversarial networks", and (a bit less starkly in terms of the alignment with the goals of this paper): Huang et al., 2017 "Stacked generative adversarial networks". - In general, comparisons to state-of-the-art (or to other) algorithms are missing. - Is the assumptions of pre-trained components viable with image, and not text, data? Please elaborate - The related work section is missing out on dozens of works, those on disentanglement or interpretability; what is the point then of making a related work section in the first place if only one single example of an algorithm in each broad topic is mentioned? If so, I would suggest mentioning this single example prior to the discussing the topic without a related work section, or (apparently the better option) to do a related work section with a rigorous coverage. Examples of some related works on disentanglement and interpretability: Higgins et al., ICLR 2017 "beta-VAE" - Kim & Mnih, ICML 2018 "Disentangling by factorising" - Adel et al., ICML 2018 "Discovering interpretable representations for both deep generative and discriminative models" - Chen et al., NIPS 2017 "InfoGAN: Interpretable representation learning by information maximizing generative adversarial nets", etc. - The advantages promised in Section 1 are a little bit too presumptuous. Too many idealistic assumptions are need in order for these advantages to hold. For instance, extensibility has been mentioned as an advantage in Section 1 and in the abstract, and that has not been capitalised on, or confirmed in the experiments, or from this point onwards. - It will be interesting to see what happens with rather real-world cases like occlusion, etc - Writing has room for improvements, in terms of both the flow and also grammar, etc. There are a few typos.
assistant
 - There have been works on this before in the GAN literature, they have not been even cited, let alone being compared to in the experiments. Seminal examples include Donahue et al., ICLR 2018 "Semantically decomposing the latent spaces of generative adversarial networks", and (a bit less starkly in terms of the alignment with the goals of this paper): Huang et al., 2017 "Stacked generative adversarial networks". - In general, comparisons to state-of-the-art (or to other) algorithms are missing. - Is the assumptions of pre-trained components viable with image, and not text, data? Please elaborate - The related work section is missing out on dozens of works, those on disentanglement or interpretability; what is the point then of making a related work section in the first place if only one single example of an algorithm in each broad topic is mentioned? If so, I would suggest mentioning this single example prior to the discussing the topic without a related work section, or (apparently the better option) to do a related work section with a rigorous coverage. Examples of some related works on disentanglement and interpretability: Higgins et al., ICLR 2017 "beta-VAE" - Kim & Mnih, ICML 2018 "Disentangling by factorising" - Adel et al., ICML 2018 "Discovering interpretable representations for both deep generative and discriminative models" - Chen et al., NIPS 2017 "InfoGAN: Interpretable representation learning by information maximizing generative adversarial nets", etc. - The advantages promised in Section 1 are a little bit too presumptuous. Too many idealistic assumptions are need in order for these advantages to hold. For instance, extensibility has been mentioned as an advantage in Section 1 and in the abstract, and that has not been capitalised on, or confirmed in the experiments, or from this point onwards. - It will be interesting to see what happens with rather real-world cases like occlusion, etc - Writing has room for improvements, in terms of both the flow and also grammar, etc. There are a few typos.