Counterfactuals uncover the modular structure of deep generative models
Deep generative models can emulate the perceptual properties of complex image datasets, providing a latent representation of the data. However, manipulating such representation to perform meaningful and controllable transformations in the data space remains challenging without some form of supervision. While previous work has focused on exploiting statistical independence to 	extit{disentangle} latent factors, we argue that such requirement can be advantageously relaxed and propose instead a non-statistical framework that relies on identifying a modular organization of the network, based on counterfactual manipulations. Our experiments support that modularity between groups of channels is achieved to a certain degree on a variety of generative models. This allowed the design of targeted interventions on complex image datasets, opening the way to applications such as computationally efficient style transfer and the automated assessment of robustness to contextual changes in pattern recognition systems.
The ideas presented in the paper are interesting and original. Whereas the theory presented has a lot of potential, it seems that the clarity of the paper could be greatly improved, in particular I would have liked more of the formal theory to be included in the body of the paper instead of relying only on the appendix. This especially matters since the theoretical aspect is a key contribution of the paper and the experimental section remains on the light side (it presents mostly examples and lacks more extensive results). I find the introduction of the proposed definition of disentanglement in sections 2.2/2.3 confusing. The authors first define “extrinsic” disentanglement of a transformation in the data space as corresponding to a transformation of one dimension only in the latent space. In section 2.3 a transformation is called “intrinsically” disentangled if it corresponds to a transformation of a subset of variables in the space of endogenous variables. It should be made clearer from the start that disentanglement is here only a property of the transformations and that the authors are not trying to define a disentangled internal representation. Further, some important questions like how to choose the reference endogenous variables and how to choose the subset E are left entirely to the experiments section. The definition of disentanglement proposed is however tied to these choices and a quick discussion would be helpful. Whereas the theory from section 2 seems precise and formal (at least in the appendix, although I did not check all the proofs), the procedure to identify modules comes with no guarantees and relies on several choices: local averaging, thresholding, nbr of clusters (the choice of this one is in my opinion well justified). Taking that into account a more extensive experimental validation would be needed to demonstrate that modules can be reliably identified. The results presented on CelebA and ImageNet are interesting, in particular using different models is a good idea, however the evaluation relies mostly on a few cases or examples and I would have liked to see more quantitative results, e.g. like in Figure 8 Appendix F. Note on related work: It has been shown (Isolating Sources of Disentanglement in VAEs by Duvenaud et al., Disentangling by Factorising by Kim et al., Challenging Common Assumptions in the Unsupervised Learning of Disentangled Representations by Bachem et al.) that Beta-VAE is far from optimal for “extrinsic” disentanglement, the text in section 4.1 should take these results into account. It would also be interesting to contrast with the following paper: Robustly Disentangled Causal Mechanisms: Validating Deep Representations for Interventional Robustness by Bauer et al. which (whilst doing something pretty different) also treats of causality and disentanglement.
You are a member of the scientific community tasked with peer review. Review the following paper title and abstract.
### Title + Abstract
Counterfactuals uncover the modular structure of deep generative models
Deep generative models can emulate the perceptual properties of complex image datasets, providing a latent representation of the data. However, manipulating such representation to perform meaningful and controllable transformations in the data space remains challenging without some form of supervision. While previous work has focused on exploiting statistical independence to 	extit{disentangle} latent factors, we argue that such requirement can be advantageously relaxed and propose instead a non-statistical framework that relies on identifying a modular organization of the network, based on counterfactual manipulations. Our experiments support that modularity between groups of channels is achieved to a certain degree on a variety of generative models. This allowed the design of targeted interventions on complex image datasets, opening the way to applications such as computationally efficient style transfer and the automated assessment of robustness to contextual changes in pattern recognition systems.
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 ideas presented in the paper are interesting and original. Whereas the theory presented has a lot of potential, it seems that the clarity of the paper could be greatly improved, in particular I would have liked more of the formal theory to be included in the body of the paper instead of relying only on the appendix. This especially matters since the theoretical aspect is a key contribution of the paper and the experimental section remains on the light side (it presents mostly examples and lacks more extensive results). I find the introduction of the proposed definition of disentanglement in sections 2.2/2.3 confusing. The authors first define “extrinsic” disentanglement of a transformation in the data space as corresponding to a transformation of one dimension only in the latent space. In section 2.3 a transformation is called “intrinsically” disentangled if it corresponds to a transformation of a subset of variables in the space of endogenous variables. It should be made clearer from the start that disentanglement is here only a property of the transformations and that the authors are not trying to define a disentangled internal representation. Further, some important questions like how to choose the reference endogenous variables and how to choose the subset E are left entirely to the experiments section. The definition of disentanglement proposed is however tied to these choices and a quick discussion would be helpful. Whereas the theory from section 2 seems precise and formal (at least in the appendix, although I did not check all the proofs), the procedure to identify modules comes with no guarantees and relies on several choices: local averaging, thresholding, nbr of clusters (the choice of this one is in my opinion well justified). Taking that into account a more extensive experimental validation would be needed to demonstrate that modules can be reliably identified. The results presented on CelebA and ImageNet are interesting, in particular using different models is a good idea, however the evaluation relies mostly on a few cases or examples and I would have liked to see more quantitative results, e.g. like in Figure 8 Appendix F. Note on related work: It has been shown (Isolating Sources of Disentanglement in VAEs by Duvenaud et al., Disentangling by Factorising by Kim et al., Challenging Common Assumptions in the Unsupervised Learning of Disentangled Representations by Bachem et al.) that Beta-VAE is far from optimal for “extrinsic” disentanglement, the text in section 4.1 should take these results into account. It would also be interesting to contrast with the following paper: Robustly Disentangled Causal Mechanisms: Validating Deep Representations for Interventional Robustness by Bauer et al. which (whilst doing something pretty different) also treats of causality and disentanglement.
assistant
The ideas presented in the paper are interesting and original. Whereas the theory presented has a lot of potential, it seems that the clarity of the paper could be greatly improved, in particular I would have liked more of the formal theory to be included in the body of the paper instead of relying only on the appendix. This especially matters since the theoretical aspect is a key contribution of the paper and the experimental section remains on the light side (it presents mostly examples and lacks more extensive results). I find the introduction of the proposed definition of disentanglement in sections 2.2/2.3 confusing. The authors first define “extrinsic” disentanglement of a transformation in the data space as corresponding to a transformation of one dimension only in the latent space. In section 2.3 a transformation is called “intrinsically” disentangled if it corresponds to a transformation of a subset of variables in the space of endogenous variables. It should be made clearer from the start that disentanglement is here only a property of the transformations and that the authors are not trying to define a disentangled internal representation. Further, some important questions like how to choose the reference endogenous variables and how to choose the subset E are left entirely to the experiments section. The definition of disentanglement proposed is however tied to these choices and a quick discussion would be helpful. Whereas the theory from section 2 seems precise and formal (at least in the appendix, although I did not check all the proofs), the procedure to identify modules comes with no guarantees and relies on several choices: local averaging, thresholding, nbr of clusters (the choice of this one is in my opinion well justified). Taking that into account a more extensive experimental validation would be needed to demonstrate that modules can be reliably identified. The results presented on CelebA and ImageNet are interesting, in particular using different models is a good idea, however the evaluation relies mostly on a few cases or examples and I would have liked to see more quantitative results, e.g. like in Figure 8 Appendix F. Note on related work: It has been shown (Isolating Sources of Disentanglement in VAEs by Duvenaud et al., Disentangling by Factorising by Kim et al., Challenging Common Assumptions in the Unsupervised Learning of Disentangled Representations by Bachem et al.) that Beta-VAE is far from optimal for “extrinsic” disentanglement, the text in section 4.1 should take these results into account. It would also be interesting to contrast with the following paper: Robustly Disentangled Causal Mechanisms: Validating Deep Representations for Interventional Robustness by Bauer et al. which (whilst doing something pretty different) also treats of causality and disentanglement.