ChoiceNet: Robust Learning by Revealing Output Correlations
In this paper, we focus on the supervised learning problem with corrupt training data. We assume that the training dataset is generated from a mixture of a target distribution and other unknown distributions. We estimate the quality of each data by revealing the correlation between the generated distribution and the target distribution. To this end, we present a novel framework referred to here as ChoiceNet that can robustly infer the target distribution in the presence of inconsistent data. We demonstrate that the proposed framework is applicable to both classification and regression tasks. Particularly, ChoiceNet is evaluated in comprehensive experiments, where we show that it constantly outperforms existing baseline methods in the handling of noisy data in synthetic regression tasks as well as behavior cloning problems. In the classification tasks, we apply the proposed method to the MNIST and CIFAR-10 datasets and it shows superior performances in terms of robustness to different types of noisy labels.
 The paper presents a framework, called ChoiceNet, for learning when the supervision outputs (e.g., labels) are corrupted by noise. The method relies on estimating the correlation between the training data distribution and a target distribution, where training data distribution is assumed to be a mixture of that target distribution and other unknown distributions. The paper also presents some compelling results on synthetic and real datasets, for both regression and classification problems. The proposed idea builds on top of previously published work on Mixture Density Networks (MDNs) and Mixup (Zhang et al, 2017). The main difference is the MDN are modified to construct the Mixture of Correlated Density Network (MCDN) block, that forms the main component of ChoiceNets. I like the overall direction and idea of modelling correlation between the target distribution and the data distribution to deal with noisy labels. The results are also compelling and I thus lean towards accepting this paper. My decision on "marginal accept" is based primarily on my unfamiliarity with this specific area and that some parts of the paper are not very easy or intuitive to read through. == Related Work == I like the related work discussion, but would emphasize more the connection to MDNs and to Mixup. Only one sentence is mentioned about Mixup but reading through the abstract and the introduction that is the first paper that came to my mind and thus I believe that it may deserve a bit more discussion. Also, there are a couple more papers that felt relevant to this work but are not mentioned: - Estimating Accuracy from Unlabeled Data: A Bayesian Approach, Platanios et al., ICML 2016. I believe this is related in how noisy labels are modeled (i.e., section 3 in the reviewed paper) and in the idea of correlation/consistency as a means to detect errors. There are couple more papers in this line of work that may be relevant. - ADIOS: Architectures Deep In Output Space, Al-Shedivat et al., ICML 2016. I believe this is related in learning some structure in the output space, even though not directly dealing with noisy labels. == Method == I believe the methods section could have been written in a more clear/easy-to-follow way, but this may also be due to my unfamiliarity with this area. Figure 1 is hard to parse and does not really offer much more than section 3.2 currently does. If the figure is improved with some more text/labels on boxes rather than plain equations, it may go a long way in making the methods section easier to follow. I would also point out MCDN as the key contribution of this paper as ChoiceNet is just any base network with an MCDN block stacked on top of this. Thus, I believe this should be emphasized more to make your key contribution clear. == Experiments == The experiments are nicely presented and are quite thorough. A couple minor comments I have are: - It would be nice to run regression experiments for bigger real-world datasets, as the ones used seem to be quite small. - I am a bit confused at the fact that in table 3 you compare your method to mixup and in table 4 you also show results when using both your method and mixup combined. Up until that point I thought that mixup was posed as an alternative method, but here it seems it*s quite orthogonal and can be used together, which I think makes sense, but would be good to clarify. Also, given that you show combined results in table 4, why not also perform exactly the same analysis for table 3 and also show numbers for CN + Mixup? It would also be nice to use the same naming scheme for both tables. I would use: ConvNet, ConvNet + CN, ConvNet + CN + Mixup, and the same with WRN for table 4. This would make the tables easier to read because currently the first thing that comes to mind is what may be different between the two setups given that they are presented side-by-side but use different naming conventions. One question that comes to mind is that you make certain assumptions on the kinds of noise your model can capture, so are there any cases where you have good intuition as to why your model may fail? It would be good to present a short discussion on this to help readers understand whether they can benefit by using your model or not.
You are a member of the scientific community tasked with peer review. Review the following paper title and abstract.
### Title + Abstract
ChoiceNet: Robust Learning by Revealing Output Correlations
In this paper, we focus on the supervised learning problem with corrupt training data. We assume that the training dataset is generated from a mixture of a target distribution and other unknown distributions. We estimate the quality of each data by revealing the correlation between the generated distribution and the target distribution. To this end, we present a novel framework referred to here as ChoiceNet that can robustly infer the target distribution in the presence of inconsistent data. We demonstrate that the proposed framework is applicable to both classification and regression tasks. Particularly, ChoiceNet is evaluated in comprehensive experiments, where we show that it constantly outperforms existing baseline methods in the handling of noisy data in synthetic regression tasks as well as behavior cloning problems. In the classification tasks, we apply the proposed method to the MNIST and CIFAR-10 datasets and it shows superior performances in terms of robustness to different types of noisy labels.
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 a framework, called ChoiceNet, for learning when the supervision outputs (e.g., labels) are corrupted by noise. The method relies on estimating the correlation between the training data distribution and a target distribution, where training data distribution is assumed to be a mixture of that target distribution and other unknown distributions. The paper also presents some compelling results on synthetic and real datasets, for both regression and classification problems. The proposed idea builds on top of previously published work on Mixture Density Networks (MDNs) and Mixup (Zhang et al, 2017). The main difference is the MDN are modified to construct the Mixture of Correlated Density Network (MCDN) block, that forms the main component of ChoiceNets. I like the overall direction and idea of modelling correlation between the target distribution and the data distribution to deal with noisy labels. The results are also compelling and I thus lean towards accepting this paper. My decision on "marginal accept" is based primarily on my unfamiliarity with this specific area and that some parts of the paper are not very easy or intuitive to read through. == Related Work == I like the related work discussion, but would emphasize more the connection to MDNs and to Mixup. Only one sentence is mentioned about Mixup but reading through the abstract and the introduction that is the first paper that came to my mind and thus I believe that it may deserve a bit more discussion. Also, there are a couple more papers that felt relevant to this work but are not mentioned: - Estimating Accuracy from Unlabeled Data: A Bayesian Approach, Platanios et al., ICML 2016. I believe this is related in how noisy labels are modeled (i.e., section 3 in the reviewed paper) and in the idea of correlation/consistency as a means to detect errors. There are couple more papers in this line of work that may be relevant. - ADIOS: Architectures Deep In Output Space, Al-Shedivat et al., ICML 2016. I believe this is related in learning some structure in the output space, even though not directly dealing with noisy labels. == Method == I believe the methods section could have been written in a more clear/easy-to-follow way, but this may also be due to my unfamiliarity with this area. Figure 1 is hard to parse and does not really offer much more than section 3.2 currently does. If the figure is improved with some more text/labels on boxes rather than plain equations, it may go a long way in making the methods section easier to follow. I would also point out MCDN as the key contribution of this paper as ChoiceNet is just any base network with an MCDN block stacked on top of this. Thus, I believe this should be emphasized more to make your key contribution clear. == Experiments == The experiments are nicely presented and are quite thorough. A couple minor comments I have are: - It would be nice to run regression experiments for bigger real-world datasets, as the ones used seem to be quite small. - I am a bit confused at the fact that in table 3 you compare your method to mixup and in table 4 you also show results when using both your method and mixup combined. Up until that point I thought that mixup was posed as an alternative method, but here it seems it*s quite orthogonal and can be used together, which I think makes sense, but would be good to clarify. Also, given that you show combined results in table 4, why not also perform exactly the same analysis for table 3 and also show numbers for CN + Mixup? It would also be nice to use the same naming scheme for both tables. I would use: ConvNet, ConvNet + CN, ConvNet + CN + Mixup, and the same with WRN for table 4. This would make the tables easier to read because currently the first thing that comes to mind is what may be different between the two setups given that they are presented side-by-side but use different naming conventions. One question that comes to mind is that you make certain assumptions on the kinds of noise your model can capture, so are there any cases where you have good intuition as to why your model may fail? It would be good to present a short discussion on this to help readers understand whether they can benefit by using your model or not.
assistant
 The paper presents a framework, called ChoiceNet, for learning when the supervision outputs (e.g., labels) are corrupted by noise. The method relies on estimating the correlation between the training data distribution and a target distribution, where training data distribution is assumed to be a mixture of that target distribution and other unknown distributions. The paper also presents some compelling results on synthetic and real datasets, for both regression and classification problems. The proposed idea builds on top of previously published work on Mixture Density Networks (MDNs) and Mixup (Zhang et al, 2017). The main difference is the MDN are modified to construct the Mixture of Correlated Density Network (MCDN) block, that forms the main component of ChoiceNets. I like the overall direction and idea of modelling correlation between the target distribution and the data distribution to deal with noisy labels. The results are also compelling and I thus lean towards accepting this paper. My decision on "marginal accept" is based primarily on my unfamiliarity with this specific area and that some parts of the paper are not very easy or intuitive to read through. == Related Work == I like the related work discussion, but would emphasize more the connection to MDNs and to Mixup. Only one sentence is mentioned about Mixup but reading through the abstract and the introduction that is the first paper that came to my mind and thus I believe that it may deserve a bit more discussion. Also, there are a couple more papers that felt relevant to this work but are not mentioned: - Estimating Accuracy from Unlabeled Data: A Bayesian Approach, Platanios et al., ICML 2016. I believe this is related in how noisy labels are modeled (i.e., section 3 in the reviewed paper) and in the idea of correlation/consistency as a means to detect errors. There are couple more papers in this line of work that may be relevant. - ADIOS: Architectures Deep In Output Space, Al-Shedivat et al., ICML 2016. I believe this is related in learning some structure in the output space, even though not directly dealing with noisy labels. == Method == I believe the methods section could have been written in a more clear/easy-to-follow way, but this may also be due to my unfamiliarity with this area. Figure 1 is hard to parse and does not really offer much more than section 3.2 currently does. If the figure is improved with some more text/labels on boxes rather than plain equations, it may go a long way in making the methods section easier to follow. I would also point out MCDN as the key contribution of this paper as ChoiceNet is just any base network with an MCDN block stacked on top of this. Thus, I believe this should be emphasized more to make your key contribution clear. == Experiments == The experiments are nicely presented and are quite thorough. A couple minor comments I have are: - It would be nice to run regression experiments for bigger real-world datasets, as the ones used seem to be quite small. - I am a bit confused at the fact that in table 3 you compare your method to mixup and in table 4 you also show results when using both your method and mixup combined. Up until that point I thought that mixup was posed as an alternative method, but here it seems it*s quite orthogonal and can be used together, which I think makes sense, but would be good to clarify. Also, given that you show combined results in table 4, why not also perform exactly the same analysis for table 3 and also show numbers for CN + Mixup? It would also be nice to use the same naming scheme for both tables. I would use: ConvNet, ConvNet + CN, ConvNet + CN + Mixup, and the same with WRN for table 4. This would make the tables easier to read because currently the first thing that comes to mind is what may be different between the two setups given that they are presented side-by-side but use different naming conventions. One question that comes to mind is that you make certain assumptions on the kinds of noise your model can capture, so are there any cases where you have good intuition as to why your model may fail? It would be good to present a short discussion on this to help readers understand whether they can benefit by using your model or not.