Challenges in Disentangling Independent Factors of Variation
We study the problem of building models that disentangle independent factors of variation. Such models encode features that can efficiently be used for classification and to transfer attributes between different images in image synthesis. As data we use a weakly labeled training set, where labels indicate what single factor has changed between two data samples, although the relative value of the change is unknown. This labeling is of particular interest as it may be readily available without annotation costs. We introduce an autoencoder model and train it through constraints on image pairs and triplets. We show the role of feature dimensionality and adversarial training theoretically and experimentally. We formally prove the existence of the reference ambiguity, which is inherently present in the disentangling task when weakly labeled data is used. The numerical value of a factor has different meaning in different reference frames. When the reference depends on other factors, transferring that factor becomes ambiguous. We demonstrate experimentally that the proposed model can successfully transfer attributes on several datasets, but show also cases when the reference ambiguity occurs.
 This paper studies the challenges of disentangling independent factors of variation under weakly labeled data. A term "reference ambiguity" is introduced, which refers to the fact that there is no guarantee that two data points with same factor of variation will be mapped to the same point if there is only weakly labeled data to that extend. I am having a hard time understanding the message of the paper. The proof in section 3.1, although elementary, is nice. But then the authors train fairly standard networks in experiments (section 4) for datasets studied with these methods in the literature, and they fail to draw any connection to the introduced reference ambiguity concept. As written, the paper to me looks like two separate white papers: {beginning - to -end of section 3}: as a theoretical white paper that lacks experiments, and {section 4}: experiments with some recent methods / datasets (this part is almost like a cute course project). Either the paper lacks to harmonically present the over arching goal, or I have missed if there was any such message that was implicit in between the lines. A rewrite with strong connection between the theory and the experiments is required.
You are a member of the scientific community tasked with peer review. Review the following paper title and abstract.
### Title + Abstract
Challenges in Disentangling Independent Factors of Variation
We study the problem of building models that disentangle independent factors of variation. Such models encode features that can efficiently be used for classification and to transfer attributes between different images in image synthesis. As data we use a weakly labeled training set, where labels indicate what single factor has changed between two data samples, although the relative value of the change is unknown. This labeling is of particular interest as it may be readily available without annotation costs. We introduce an autoencoder model and train it through constraints on image pairs and triplets. We show the role of feature dimensionality and adversarial training theoretically and experimentally. We formally prove the existence of the reference ambiguity, which is inherently present in the disentangling task when weakly labeled data is used. The numerical value of a factor has different meaning in different reference frames. When the reference depends on other factors, transferring that factor becomes ambiguous. We demonstrate experimentally that the proposed model can successfully transfer attributes on several datasets, but show also cases when the reference ambiguity occurs.
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 studies the challenges of disentangling independent factors of variation under weakly labeled data. A term "reference ambiguity" is introduced, which refers to the fact that there is no guarantee that two data points with same factor of variation will be mapped to the same point if there is only weakly labeled data to that extend. I am having a hard time understanding the message of the paper. The proof in section 3.1, although elementary, is nice. But then the authors train fairly standard networks in experiments (section 4) for datasets studied with these methods in the literature, and they fail to draw any connection to the introduced reference ambiguity concept. As written, the paper to me looks like two separate white papers: {beginning - to -end of section 3}: as a theoretical white paper that lacks experiments, and {section 4}: experiments with some recent methods / datasets (this part is almost like a cute course project). Either the paper lacks to harmonically present the over arching goal, or I have missed if there was any such message that was implicit in between the lines. A rewrite with strong connection between the theory and the experiments is required.
assistant
 This paper studies the challenges of disentangling independent factors of variation under weakly labeled data. A term "reference ambiguity" is introduced, which refers to the fact that there is no guarantee that two data points with same factor of variation will be mapped to the same point if there is only weakly labeled data to that extend. I am having a hard time understanding the message of the paper. The proof in section 3.1, although elementary, is nice. But then the authors train fairly standard networks in experiments (section 4) for datasets studied with these methods in the literature, and they fail to draw any connection to the introduced reference ambiguity concept. As written, the paper to me looks like two separate white papers: {beginning - to -end of section 3}: as a theoretical white paper that lacks experiments, and {section 4}: experiments with some recent methods / datasets (this part is almost like a cute course project). Either the paper lacks to harmonically present the over arching goal, or I have missed if there was any such message that was implicit in between the lines. A rewrite with strong connection between the theory and the experiments is required.