Preventing Posterior Collapse with delta-VAEs
Due to the phenomenon of “posterior collapse,” current latent variable generative models pose a challenging design choice that either weakens the capacity of the decoder or requires altering the training objective. We develop an alternative that utilizes the most powerful generative models as decoders, optimize the variational lower bound, and ensures that the latent variables preserve and encode useful information. Our proposed δ-VAEs achieve this by constraining the variational family for the posterior to have a minimum distance to the prior. For sequential latent variable models, our approach resembles the classic representation learning approach of slow feature analysis. We demonstrate our method’s efficacy at modeling text on LM1B and modeling images: learning representations, improving sample quality, and achieving state of the art log-likelihood on CIFAR-10 and ImageNet 32 × 32.
 The majority of approaches for preventing posterior collapse in VAEs equipped with powerful decoders to better model local structure involve either: alteration of the ELBO training objective, or a restriction on the decoder structure. This paper presents an approach which broadly falls into the latter category; by limiting the family of the variational approximation to the posterior, the minimum KL divergence between the prior and posterior is lower bounded to a *delta* value, preventing collapse. The paper is well written, and the methodology clearly explained. The experiments show that the proposed approach (delta VAE combined with the *anti-causal* architecture) captures both local and global structure, and appears to do so while preserving SOTA discriminative performance on some tasks. Tests are performed on both generative image and language tasks. I believe that the paper is of low-medium significance: whilst it does outline a different method of restricting the family of posteriors, it does not give a detailed reasoning (empirical or theoretical) as to why this should be a generally better solution as compared to other approaches. Pros: - Very clear and well written. - Good execution and ablation/experimentation section. Cons: - Lack of theory (and minimal experimentation) as to why this approach should be better than competing methods.
You are a member of the scientific community tasked with peer review. Review the following paper title and abstract.
### Title + Abstract
Preventing Posterior Collapse with delta-VAEs
Due to the phenomenon of “posterior collapse,” current latent variable generative models pose a challenging design choice that either weakens the capacity of the decoder or requires altering the training objective. We develop an alternative that utilizes the most powerful generative models as decoders, optimize the variational lower bound, and ensures that the latent variables preserve and encode useful information. Our proposed δ-VAEs achieve this by constraining the variational family for the posterior to have a minimum distance to the prior. For sequential latent variable models, our approach resembles the classic representation learning approach of slow feature analysis. We demonstrate our method’s efficacy at modeling text on LM1B and modeling images: learning representations, improving sample quality, and achieving state of the art log-likelihood on CIFAR-10 and ImageNet 32 × 32.
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 majority of approaches for preventing posterior collapse in VAEs equipped with powerful decoders to better model local structure involve either: alteration of the ELBO training objective, or a restriction on the decoder structure. This paper presents an approach which broadly falls into the latter category; by limiting the family of the variational approximation to the posterior, the minimum KL divergence between the prior and posterior is lower bounded to a *delta* value, preventing collapse. The paper is well written, and the methodology clearly explained. The experiments show that the proposed approach (delta VAE combined with the *anti-causal* architecture) captures both local and global structure, and appears to do so while preserving SOTA discriminative performance on some tasks. Tests are performed on both generative image and language tasks. I believe that the paper is of low-medium significance: whilst it does outline a different method of restricting the family of posteriors, it does not give a detailed reasoning (empirical or theoretical) as to why this should be a generally better solution as compared to other approaches. Pros: - Very clear and well written. - Good execution and ablation/experimentation section. Cons: - Lack of theory (and minimal experimentation) as to why this approach should be better than competing methods.
assistant
 The majority of approaches for preventing posterior collapse in VAEs equipped with powerful decoders to better model local structure involve either: alteration of the ELBO training objective, or a restriction on the decoder structure. This paper presents an approach which broadly falls into the latter category; by limiting the family of the variational approximation to the posterior, the minimum KL divergence between the prior and posterior is lower bounded to a *delta* value, preventing collapse. The paper is well written, and the methodology clearly explained. The experiments show that the proposed approach (delta VAE combined with the *anti-causal* architecture) captures both local and global structure, and appears to do so while preserving SOTA discriminative performance on some tasks. Tests are performed on both generative image and language tasks. I believe that the paper is of low-medium significance: whilst it does outline a different method of restricting the family of posteriors, it does not give a detailed reasoning (empirical or theoretical) as to why this should be a generally better solution as compared to other approaches. Pros: - Very clear and well written. - Good execution and ablation/experimentation section. Cons: - Lack of theory (and minimal experimentation) as to why this approach should be better than competing methods.