Disentangling Factors of Variations Using Few Labels
Learning disentangled representations is considered a cornerstone problem in representation learning. Recently, Locatello et al. (2019) demonstrated that unsupervised disentanglement learning without inductive biases is theoretically impossible and that existing inductive biases and unsupervised methods do not allow to consistently learn disentangled representations. However, in many practical settings, one might have access to a limited amount of supervision, for example through manual labeling of (some) factors of variation in a few training examples. In this paper, we investigate the impact of such supervision on state-of-the-art disentanglement methods and perform a large scale study, training over 52000 models under well-defined and reproducible experimental conditions. We observe that a small number of labeled examples (0.01--0.5% of the data set), with potentially imprecise and incomplete labels, is sufficient to perform model selection on state-of-the-art unsupervised models. Further, we investigate the benefit of incorporating supervision into the training process. Overall, we empirically validate that with little and imprecise supervision it is possible to reliably learn disentangled representations.
The paper presents evidence that even a tiny bit of supervision over the factors of variation in a dataset presented in the form of semi-supervised training labels or for unsupervised model selection, can result in models that learn disentangled representations. The authors perform a thorough sweep over multiple datasets, different models classes and ways to provide labeled information. Overall, this work is a well executed and rigorous empirical study on the state of disentangled representation learning. I think experimental protocol and models trained models will prove extremely useful for future work and advocate for accepting this paper. Comments 1) Would it be possible to use the few labeled factors of variation in a meta-learning setup rather than as a regularizer? 2) The paper provides high level conclusions about the impact of having supervised model selection or semi-supervised learning in models in general, but doesn’t offer much discussion into their behavior under specific settings (i.e.) it seems to be hard to pick a winner amongst presented model. Some are better with 100 labeled examples but don’t scale as well as others when an order of magnitude more labeled data is available. It is certainly hard to discuss all the thousands of experimental observations, but the paper can benefit from some more fine-grained analysis. Minor Figure 4 is hard to comprehend without a model to index mapping similar to Figure 3
You are a member of the scientific community tasked with peer review. Review the following paper title and abstract.
### Title + Abstract
Disentangling Factors of Variations Using Few Labels
Learning disentangled representations is considered a cornerstone problem in representation learning. Recently, Locatello et al. (2019) demonstrated that unsupervised disentanglement learning without inductive biases is theoretically impossible and that existing inductive biases and unsupervised methods do not allow to consistently learn disentangled representations. However, in many practical settings, one might have access to a limited amount of supervision, for example through manual labeling of (some) factors of variation in a few training examples. In this paper, we investigate the impact of such supervision on state-of-the-art disentanglement methods and perform a large scale study, training over 52000 models under well-defined and reproducible experimental conditions. We observe that a small number of labeled examples (0.01--0.5% of the data set), with potentially imprecise and incomplete labels, is sufficient to perform model selection on state-of-the-art unsupervised models. Further, we investigate the benefit of incorporating supervision into the training process. Overall, we empirically validate that with little and imprecise supervision it is possible to reliably learn disentangled representations.
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 presents evidence that even a tiny bit of supervision over the factors of variation in a dataset presented in the form of semi-supervised training labels or for unsupervised model selection, can result in models that learn disentangled representations. The authors perform a thorough sweep over multiple datasets, different models classes and ways to provide labeled information. Overall, this work is a well executed and rigorous empirical study on the state of disentangled representation learning. I think experimental protocol and models trained models will prove extremely useful for future work and advocate for accepting this paper. Comments 1) Would it be possible to use the few labeled factors of variation in a meta-learning setup rather than as a regularizer? 2) The paper provides high level conclusions about the impact of having supervised model selection or semi-supervised learning in models in general, but doesn’t offer much discussion into their behavior under specific settings (i.e.) it seems to be hard to pick a winner amongst presented model. Some are better with 100 labeled examples but don’t scale as well as others when an order of magnitude more labeled data is available. It is certainly hard to discuss all the thousands of experimental observations, but the paper can benefit from some more fine-grained analysis. Minor Figure 4 is hard to comprehend without a model to index mapping similar to Figure 3
assistant
The paper presents evidence that even a tiny bit of supervision over the factors of variation in a dataset presented in the form of semi-supervised training labels or for unsupervised model selection, can result in models that learn disentangled representations. The authors perform a thorough sweep over multiple datasets, different models classes and ways to provide labeled information. Overall, this work is a well executed and rigorous empirical study on the state of disentangled representation learning. I think experimental protocol and models trained models will prove extremely useful for future work and advocate for accepting this paper. Comments 1) Would it be possible to use the few labeled factors of variation in a meta-learning setup rather than as a regularizer? 2) The paper provides high level conclusions about the impact of having supervised model selection or semi-supervised learning in models in general, but doesn’t offer much discussion into their behavior under specific settings (i.e.) it seems to be hard to pick a winner amongst presented model. Some are better with 100 labeled examples but don’t scale as well as others when an order of magnitude more labeled data is available. It is certainly hard to discuss all the thousands of experimental observations, but the paper can benefit from some more fine-grained analysis. Minor Figure 4 is hard to comprehend without a model to index mapping similar to Figure 3