Explicit Information Placement on Latent Variables using Auxiliary Generative Modelling Task
Deep latent variable models, such as variational autoencoders, have been successfully used to disentangle factors of variation in image datasets. The structure of the representations learned by such models is usually observed after training and iteratively refined by tuning the network architecture and loss function. Here we propose a method that can explicitly place information into a specific subset of the latent variables. We demonstrate the use of the method in a task of disentangling global structure from local features in images. One subset of the latent variables is encouraged to represent local features through an auxiliary modelling task. In this auxiliary task, the global structure of an image is destroyed by dividing it into pixel patches which are then randomly shuffled. The full set of latent variables is trained to model the original data, obliging the remainder of the latent representation to model the global structure. We demonstrate that this approach successfully disentangles the latent variables for global structure from local structure by observing the generative samples of SVHN and CIFAR10. We also clustering the disentangled global structure of SVHN and found that the emerging clusters represent meaningful groups of global structures – including digit identities and the number of digits presence. Finally, we discuss the problem of evaluating the clustering accuracy when ground truth categories are not expressive enough.
 The paper proposes a method to disentangle latent variables for certain factors of interest in an image by considering the original input image and a transformation of the image where information about the factors of interest is removed. The generative process is then modeled by having two latent variables -- the first responsible for generating the transformed image whereas both latent variables are responsible for generating the original input image. This inductive bias naturally enforces that the second latent variable will not model the information which the first needs to reconstruct the transformed image, due to the VAE objective penalizing redundancy in information present in the latents. The paper demonstrates this in one setting where the transformation is random shuffling of image patches, which should remove the global information of the original input image. The methodology of the paper was concise and easy to follow. The simple inductive bias presented in the paper for disentangling local and global information is very interesting. It is not obvious that shuffling image patches at a particular scale would lead to complete loss of global information, but the paper does show results on SVHN and CIFAR10 for which global information is sufficiently disentangled. The results for digit identity clustering were great for showing the correlation between their learnt global information and label information. The paper introduced their model as a general purpose strategy for placing desired information in latent variables using auxiliary tasks, but focus was directed to the global vs local line of analysis. While giving examples for what kind of information can be removed, the authors mentioned that color to gray-scale might be one possibility. It would have been interesting to see this and other possibilities explored in the paper. I feel that the idea deserves a broader analysis beyond just a single choice of disentanglement. It is mentioned in the paper that having a single inference network for the posterior as opposed to the factorized one is conceivable. I would be curious to see an analysis of how that works out as compared to the separate encoders case. Overall, the paper has a novel idea which is well motivated and executed in terms of experiments.
You are a member of the scientific community tasked with peer review. Review the following paper title and abstract.
### Title + Abstract
Explicit Information Placement on Latent Variables using Auxiliary Generative Modelling Task
Deep latent variable models, such as variational autoencoders, have been successfully used to disentangle factors of variation in image datasets. The structure of the representations learned by such models is usually observed after training and iteratively refined by tuning the network architecture and loss function. Here we propose a method that can explicitly place information into a specific subset of the latent variables. We demonstrate the use of the method in a task of disentangling global structure from local features in images. One subset of the latent variables is encouraged to represent local features through an auxiliary modelling task. In this auxiliary task, the global structure of an image is destroyed by dividing it into pixel patches which are then randomly shuffled. The full set of latent variables is trained to model the original data, obliging the remainder of the latent representation to model the global structure. We demonstrate that this approach successfully disentangles the latent variables for global structure from local structure by observing the generative samples of SVHN and CIFAR10. We also clustering the disentangled global structure of SVHN and found that the emerging clusters represent meaningful groups of global structures – including digit identities and the number of digits presence. Finally, we discuss the problem of evaluating the clustering accuracy when ground truth categories are not expressive enough.
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 paper proposes a method to disentangle latent variables for certain factors of interest in an image by considering the original input image and a transformation of the image where information about the factors of interest is removed. The generative process is then modeled by having two latent variables -- the first responsible for generating the transformed image whereas both latent variables are responsible for generating the original input image. This inductive bias naturally enforces that the second latent variable will not model the information which the first needs to reconstruct the transformed image, due to the VAE objective penalizing redundancy in information present in the latents. The paper demonstrates this in one setting where the transformation is random shuffling of image patches, which should remove the global information of the original input image. The methodology of the paper was concise and easy to follow. The simple inductive bias presented in the paper for disentangling local and global information is very interesting. It is not obvious that shuffling image patches at a particular scale would lead to complete loss of global information, but the paper does show results on SVHN and CIFAR10 for which global information is sufficiently disentangled. The results for digit identity clustering were great for showing the correlation between their learnt global information and label information. The paper introduced their model as a general purpose strategy for placing desired information in latent variables using auxiliary tasks, but focus was directed to the global vs local line of analysis. While giving examples for what kind of information can be removed, the authors mentioned that color to gray-scale might be one possibility. It would have been interesting to see this and other possibilities explored in the paper. I feel that the idea deserves a broader analysis beyond just a single choice of disentanglement. It is mentioned in the paper that having a single inference network for the posterior as opposed to the factorized one is conceivable. I would be curious to see an analysis of how that works out as compared to the separate encoders case. Overall, the paper has a novel idea which is well motivated and executed in terms of experiments.
assistant
 The paper proposes a method to disentangle latent variables for certain factors of interest in an image by considering the original input image and a transformation of the image where information about the factors of interest is removed. The generative process is then modeled by having two latent variables -- the first responsible for generating the transformed image whereas both latent variables are responsible for generating the original input image. This inductive bias naturally enforces that the second latent variable will not model the information which the first needs to reconstruct the transformed image, due to the VAE objective penalizing redundancy in information present in the latents. The paper demonstrates this in one setting where the transformation is random shuffling of image patches, which should remove the global information of the original input image. The methodology of the paper was concise and easy to follow. The simple inductive bias presented in the paper for disentangling local and global information is very interesting. It is not obvious that shuffling image patches at a particular scale would lead to complete loss of global information, but the paper does show results on SVHN and CIFAR10 for which global information is sufficiently disentangled. The results for digit identity clustering were great for showing the correlation between their learnt global information and label information. The paper introduced their model as a general purpose strategy for placing desired information in latent variables using auxiliary tasks, but focus was directed to the global vs local line of analysis. While giving examples for what kind of information can be removed, the authors mentioned that color to gray-scale might be one possibility. It would have been interesting to see this and other possibilities explored in the paper. I feel that the idea deserves a broader analysis beyond just a single choice of disentanglement. It is mentioned in the paper that having a single inference network for the posterior as opposed to the factorized one is conceivable. I would be curious to see an analysis of how that works out as compared to the separate encoders case. Overall, the paper has a novel idea which is well motivated and executed in terms of experiments.