Semi-Supervised Learning with Normalizing Flows
We propose Flow Gaussian Mixture Model (FlowGMM), a general-purpose method for semi-supervised learning based on a simple and principled probabilistic framework. We approximate the joint distribution of the labeled and unlabeled data with a flexible mixture model implemented as a Gaussian mixture transformed by a normalizing flow. We train the model by maximizing the exact joint likelihood of the labeled and unlabeled data. We evaluate FlowGMM on a wide range of semi-supervised classification problems across different data types: AG-News and Yahoo Answers text data, MNIST, SVHN and CIFAR-10 image classification problems as well as tabular UCI datasets. FlowGMM achieves promising results on image classification problems and outperforms the competing methods on other types of data. FlowGMM learns an interpretable latent repesentation space and allows hyper-parameter free feature visualization at real time rates. Finally, we show that FlowGMM can be calibrated to produce meaningful uncertainty estimates for its predictions.
The authors propose a semi-supervised learning model, named as Flow Gaussian Mixture Model (FlowGMM). The model is learnt by maximizing the join likelihood of the labeled and unlabeled data with a consistency regularization. The authors demonstrate that the proposed model outperforms others on text classification; for image classification, the performance can be improved in future. Also the authors demonstrate that the model is interpretable via feature visualizations. Overall the paper is fine written. Yet, The conclusion is not fairly supported and the paper could be much stronger with the issues discussed already but I don’t think its current form is ready yet. Below are more detailed comments: 0) It would be nice to add the definition of the performance metric; without the definitions, none of the numbers in the tables would make sense. 1) The main result for text classification in Table 1 is reporting the best of 3 runs, which can’t support the conclusion that the proposed method outperforms the other. In general, it’s nice to provide statistical significance comparing two models or reporting the mean and std across multiple runs. 2) In Table 2, it’s not clear what conclusion could be drawn by comparing the performance of supervised and semi-supervised performance. Are the testing data points the same? 3) The feature visualization as discussed in Section 6.3 is not explained clear. Specifically, “giving us insight into the workings of the model” is not clear; what exactly insight can we get and what exactly are the workings can we get?
You are a member of the scientific community tasked with peer review. Review the following paper title and abstract.
### Title + Abstract
Semi-Supervised Learning with Normalizing Flows
We propose Flow Gaussian Mixture Model (FlowGMM), a general-purpose method for semi-supervised learning based on a simple and principled probabilistic framework. We approximate the joint distribution of the labeled and unlabeled data with a flexible mixture model implemented as a Gaussian mixture transformed by a normalizing flow. We train the model by maximizing the exact joint likelihood of the labeled and unlabeled data. We evaluate FlowGMM on a wide range of semi-supervised classification problems across different data types: AG-News and Yahoo Answers text data, MNIST, SVHN and CIFAR-10 image classification problems as well as tabular UCI datasets. FlowGMM achieves promising results on image classification problems and outperforms the competing methods on other types of data. FlowGMM learns an interpretable latent repesentation space and allows hyper-parameter free feature visualization at real time rates. Finally, we show that FlowGMM can be calibrated to produce meaningful uncertainty estimates for its predictions.
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 authors propose a semi-supervised learning model, named as Flow Gaussian Mixture Model (FlowGMM). The model is learnt by maximizing the join likelihood of the labeled and unlabeled data with a consistency regularization. The authors demonstrate that the proposed model outperforms others on text classification; for image classification, the performance can be improved in future. Also the authors demonstrate that the model is interpretable via feature visualizations. Overall the paper is fine written. Yet, The conclusion is not fairly supported and the paper could be much stronger with the issues discussed already but I don’t think its current form is ready yet. Below are more detailed comments: 0) It would be nice to add the definition of the performance metric; without the definitions, none of the numbers in the tables would make sense. 1) The main result for text classification in Table 1 is reporting the best of 3 runs, which can’t support the conclusion that the proposed method outperforms the other. In general, it’s nice to provide statistical significance comparing two models or reporting the mean and std across multiple runs. 2) In Table 2, it’s not clear what conclusion could be drawn by comparing the performance of supervised and semi-supervised performance. Are the testing data points the same? 3) The feature visualization as discussed in Section 6.3 is not explained clear. Specifically, “giving us insight into the workings of the model” is not clear; what exactly insight can we get and what exactly are the workings can we get?
assistant
The authors propose a semi-supervised learning model, named as Flow Gaussian Mixture Model (FlowGMM). The model is learnt by maximizing the join likelihood of the labeled and unlabeled data with a consistency regularization. The authors demonstrate that the proposed model outperforms others on text classification; for image classification, the performance can be improved in future. Also the authors demonstrate that the model is interpretable via feature visualizations. Overall the paper is fine written. Yet, The conclusion is not fairly supported and the paper could be much stronger with the issues discussed already but I don’t think its current form is ready yet. Below are more detailed comments: 0) It would be nice to add the definition of the performance metric; without the definitions, none of the numbers in the tables would make sense. 1) The main result for text classification in Table 1 is reporting the best of 3 runs, which can’t support the conclusion that the proposed method outperforms the other. In general, it’s nice to provide statistical significance comparing two models or reporting the mean and std across multiple runs. 2) In Table 2, it’s not clear what conclusion could be drawn by comparing the performance of supervised and semi-supervised performance. Are the testing data points the same? 3) The feature visualization as discussed in Section 6.3 is not explained clear. Specifically, “giving us insight into the workings of the model” is not clear; what exactly insight can we get and what exactly are the workings can we get?