Bridging ELBO objective and MMD
One of the challenges in training generative models such as the variational auto encoder (VAE) is avoiding posterior collapse. When the generator has too much capacity, it is prone to ignoring latent code. This problem is exacerbated when the dataset is small, and the latent dimension is high. The root of the problem is the ELBO objective, specifically the Kullback–Leibler (KL) divergence term in objective function. This paper proposes a new objective function to replace the KL term with one that emulates the maximum mean discrepancy (MMD) objective. It also introduces a new technique, named latent clipping, that is used to control distance between samples in latent space. A probabilistic autoencoder model, named -VAE, is designed and trained on MNIST and MNIST Fashion datasets, using the new objective function and is shown to outperform models trained with ELBO and -VAE objective. The -VAE is less prone to posterior collapse, and can generate reconstructions and new samples in good quality. Latent representations learned by -VAE are shown to be good and can be used for downstream tasks such as classification.
This paper proposes to modify the ELBO loss for VAE in order to learn better latent representation of the data. There is a large literature on this topic and the related work section should be written with more care and details. Some references are missing: [a] Fixing a Broken ELBO by Alemi et al. [b] Learning Disentangled Joint Continuous and Discrete Representations by Dupont On a general level, the motivation for this work is not completely clear. As mentioned in the related work section, one could use a more complex prior such as a mixture of Gaussians or an architecture like [b] to do clustering. At least, in the experiment section, results shuold be compared with these natrual approaches. It should be easy as [b] for example provided his code and gives results on the same datasets as those used here. Another remark is that the title is quite misleading. The connection with MMD is very weak in my opinion. Indeed modifing the KL term in the loss by using MMD has already been proposed in Infovae (last reference of this paper) and in InfoVAE a MMD-VAE is also introduced. This connection should be explained and comparison should be given with this work.
You are a member of the scientific community tasked with peer review. Review the following paper title and abstract.
### Title + Abstract
Bridging ELBO objective and MMD
One of the challenges in training generative models such as the variational auto encoder (VAE) is avoiding posterior collapse. When the generator has too much capacity, it is prone to ignoring latent code. This problem is exacerbated when the dataset is small, and the latent dimension is high. The root of the problem is the ELBO objective, specifically the Kullback–Leibler (KL) divergence term in objective function. This paper proposes a new objective function to replace the KL term with one that emulates the maximum mean discrepancy (MMD) objective. It also introduces a new technique, named latent clipping, that is used to control distance between samples in latent space. A probabilistic autoencoder model, named -VAE, is designed and trained on MNIST and MNIST Fashion datasets, using the new objective function and is shown to outperform models trained with ELBO and -VAE objective. The -VAE is less prone to posterior collapse, and can generate reconstructions and new samples in good quality. Latent representations learned by -VAE are shown to be good and can be used for downstream tasks such as classification.
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>



This paper proposes to modify the ELBO loss for VAE in order to learn better latent representation of the data. There is a large literature on this topic and the related work section should be written with more care and details. Some references are missing: [a] Fixing a Broken ELBO by Alemi et al. [b] Learning Disentangled Joint Continuous and Discrete Representations by Dupont On a general level, the motivation for this work is not completely clear. As mentioned in the related work section, one could use a more complex prior such as a mixture of Gaussians or an architecture like [b] to do clustering. At least, in the experiment section, results shuold be compared with these natrual approaches. It should be easy as [b] for example provided his code and gives results on the same datasets as those used here. Another remark is that the title is quite misleading. The connection with MMD is very weak in my opinion. Indeed modifing the KL term in the loss by using MMD has already been proposed in Infovae (last reference of this paper) and in InfoVAE a MMD-VAE is also introduced. This connection should be explained and comparison should be given with this work.
assistant
This paper proposes to modify the ELBO loss for VAE in order to learn better latent representation of the data. There is a large literature on this topic and the related work section should be written with more care and details. Some references are missing: [a] Fixing a Broken ELBO by Alemi et al. [b] Learning Disentangled Joint Continuous and Discrete Representations by Dupont On a general level, the motivation for this work is not completely clear. As mentioned in the related work section, one could use a more complex prior such as a mixture of Gaussians or an architecture like [b] to do clustering. At least, in the experiment section, results shuold be compared with these natrual approaches. It should be easy as [b] for example provided his code and gives results on the same datasets as those used here. Another remark is that the title is quite misleading. The connection with MMD is very weak in my opinion. Indeed modifing the KL term in the loss by using MMD has already been proposed in Infovae (last reference of this paper) and in InfoVAE a MMD-VAE is also introduced. This connection should be explained and comparison should be given with this work.