Variational Domain Adaptation
This paper proposes variational domain adaptation, a unified, scalable, simple framework for learning multiple distributions through variational inference. Unlike the existing methods on domain transfer through deep generative models, such as StarGAN (Choi et al., 2017) and UFDN (Liu et al., 2018), the variational domain adaptation has three advantages. Firstly, the samples from the target are not required. Instead, the framework requires one known source as a prior and binary discriminators, , discriminating the target domain from others. Consequently, the framework regards a target as a posterior that can be explicitly formulated through the Bayesian inference, , as exhibited by a further proposed model of dual variational autoencoder (DualVAE). Secondly, the framework is scablable to large-scale domains. As well as VAE encodes a sample as a mode on a latent space: , DualVAE encodes a domain as a mode on the dual latent space , named domain embedding. It reformulates the posterior with a natural paring , which can be expanded to uncountable infinite domains such as continuous domains as well as interpolation. Thirdly, DualVAE fastly converges without sophisticated automatic/manual hyperparameter search in comparison to GANs as it requires only one additional parameter to VAE. Through the numerical experiment, we demonstrate the three benefits with multi-domain image generation task on CelebA with up to 60 domains, and exhibits that DualVAE records the state-of-the-art performance outperforming StarGAN and UFDN.
 - To this reviewer’s understanding, the proposed method is very similar or equal to the conditional VAE. The only difference comes from the way of involving the condition information during training. This should be clarified and further, it is necessary to compare with the conditional VAE in the experiments, rather than the vanilla VAE. - The proposed method uses a predefined and fixed value of the variance , which is very informative and should be estimated from data in inference. Basically, there is no specification on this value in their experiments. - In a similar perspective, how the results changes according to the variation of the value . - It is not intuitive how significant the improvement of 5% in PIS. It would be good to provide the intuitive understanding of the improvement.
You are a member of the scientific community tasked with peer review. Review the following paper title and abstract.
### Title + Abstract
Variational Domain Adaptation
This paper proposes variational domain adaptation, a unified, scalable, simple framework for learning multiple distributions through variational inference. Unlike the existing methods on domain transfer through deep generative models, such as StarGAN (Choi et al., 2017) and UFDN (Liu et al., 2018), the variational domain adaptation has three advantages. Firstly, the samples from the target are not required. Instead, the framework requires one known source as a prior and binary discriminators, , discriminating the target domain from others. Consequently, the framework regards a target as a posterior that can be explicitly formulated through the Bayesian inference, , as exhibited by a further proposed model of dual variational autoencoder (DualVAE). Secondly, the framework is scablable to large-scale domains. As well as VAE encodes a sample as a mode on a latent space: , DualVAE encodes a domain as a mode on the dual latent space , named domain embedding. It reformulates the posterior with a natural paring , which can be expanded to uncountable infinite domains such as continuous domains as well as interpolation. Thirdly, DualVAE fastly converges without sophisticated automatic/manual hyperparameter search in comparison to GANs as it requires only one additional parameter to VAE. Through the numerical experiment, we demonstrate the three benefits with multi-domain image generation task on CelebA with up to 60 domains, and exhibits that DualVAE records the state-of-the-art performance outperforming StarGAN and UFDN.
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>



 - To this reviewer’s understanding, the proposed method is very similar or equal to the conditional VAE. The only difference comes from the way of involving the condition information during training. This should be clarified and further, it is necessary to compare with the conditional VAE in the experiments, rather than the vanilla VAE. - The proposed method uses a predefined and fixed value of the variance , which is very informative and should be estimated from data in inference. Basically, there is no specification on this value in their experiments. - In a similar perspective, how the results changes according to the variation of the value . - It is not intuitive how significant the improvement of 5% in PIS. It would be good to provide the intuitive understanding of the improvement.
assistant
 - To this reviewer’s understanding, the proposed method is very similar or equal to the conditional VAE. The only difference comes from the way of involving the condition information during training. This should be clarified and further, it is necessary to compare with the conditional VAE in the experiments, rather than the vanilla VAE. - The proposed method uses a predefined and fixed value of the variance , which is very informative and should be estimated from data in inference. Basically, there is no specification on this value in their experiments. - In a similar perspective, how the results changes according to the variation of the value . - It is not intuitive how significant the improvement of 5% in PIS. It would be good to provide the intuitive understanding of the improvement.