Generalized Convolutional Forest Networks for Domain Generalization and Visual Recognition
When constructing random forests, it is of prime importance to ensure high accuracy and low correlation of individual tree classifiers for good performance. Nevertheless, it is typically difficult for existing random forest methods to strike a good balance between these conflicting factors. In this work, we propose a generalized convolutional forest networks to learn a feature space to maximize the strength of individual tree classifiers while minimizing the respective correlation. The feature space is iteratively constructed by a probabilistic triplet sampling method based on the distribution obtained from the splits of the random forest. The sampling process is designed to pull the data of the same label together for higher strength and push away the data frequently falling to the same leaf nodes. We perform extensive experiments on five image classification and two domain generalization datasets with ResNet-50 and DenseNet-161 backbone networks. Experimental results show that the proposed algorithm performs favorably against state-of-the-art methods.
The paper proposes a scheme to learn representations and a random forest model in a closely coupled manner, where the representations are learned to simultaneously improve the predictive performance of the individual trees in the random forest and reduce the correlation between these trees. This is achieved via a novel triplet sampling scheme where the positive samples are used to improve the predictive performance of the individual trees, while the negative samples are used to reduce the correlation between the trees. The empirical results indicate the improvement of the proposed scheme over random forests learned over the original representations across various datasets. The manuscript is well presented and I am leaning towards accept, one major issue with this manuscript is the number of pages which crosses the 8 page recommended limit in the ICLR CfP. Given that higher standard, I feel that there are multiple minor issues which should be addressed. - It is not clear why the proposed scheme is especially well suited for *domain generalization* and *visual recognition* and not for general supervised classification tasks (like those on CIFAR, SVHN, etc). This needs to be clarified. - The theoretical results for random forest rely on the independence of the individual trees. Given that the trees in the random forest are no longer independent in the proposed scheme, the upper bound in Equation (1) may no longer be valid. While this does not affect the empirical performance, it might be good to discuss the theoretical implications of the proposed scheme. - It is not clear why the curves for the canonical features in Figure 3 not improving with the number of iterations (which corresponds to number of trees to the best of my understanding). The results in Table 1 do indicate improvement in the performance with canonical features, putting Figure 3 and Table 1 at odds. Minor: - The individual trees in a random forest are supposed to have low bias but high variance, and the heterogeneity between the trees is supposed to squash the variance with the bagging. The introduction mentions that the individual trees in the forest are *less biased*. Maybe this is because of our different definitions of bias but any clarification here would be helpful.
You are a member of the scientific community tasked with peer review. Review the following paper title and abstract.
### Title + Abstract
Generalized Convolutional Forest Networks for Domain Generalization and Visual Recognition
When constructing random forests, it is of prime importance to ensure high accuracy and low correlation of individual tree classifiers for good performance. Nevertheless, it is typically difficult for existing random forest methods to strike a good balance between these conflicting factors. In this work, we propose a generalized convolutional forest networks to learn a feature space to maximize the strength of individual tree classifiers while minimizing the respective correlation. The feature space is iteratively constructed by a probabilistic triplet sampling method based on the distribution obtained from the splits of the random forest. The sampling process is designed to pull the data of the same label together for higher strength and push away the data frequently falling to the same leaf nodes. We perform extensive experiments on five image classification and two domain generalization datasets with ResNet-50 and DenseNet-161 backbone networks. Experimental results show that the proposed algorithm performs favorably against state-of-the-art methods.
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 proposes a scheme to learn representations and a random forest model in a closely coupled manner, where the representations are learned to simultaneously improve the predictive performance of the individual trees in the random forest and reduce the correlation between these trees. This is achieved via a novel triplet sampling scheme where the positive samples are used to improve the predictive performance of the individual trees, while the negative samples are used to reduce the correlation between the trees. The empirical results indicate the improvement of the proposed scheme over random forests learned over the original representations across various datasets. The manuscript is well presented and I am leaning towards accept, one major issue with this manuscript is the number of pages which crosses the 8 page recommended limit in the ICLR CfP. Given that higher standard, I feel that there are multiple minor issues which should be addressed. - It is not clear why the proposed scheme is especially well suited for *domain generalization* and *visual recognition* and not for general supervised classification tasks (like those on CIFAR, SVHN, etc). This needs to be clarified. - The theoretical results for random forest rely on the independence of the individual trees. Given that the trees in the random forest are no longer independent in the proposed scheme, the upper bound in Equation (1) may no longer be valid. While this does not affect the empirical performance, it might be good to discuss the theoretical implications of the proposed scheme. - It is not clear why the curves for the canonical features in Figure 3 not improving with the number of iterations (which corresponds to number of trees to the best of my understanding). The results in Table 1 do indicate improvement in the performance with canonical features, putting Figure 3 and Table 1 at odds. Minor: - The individual trees in a random forest are supposed to have low bias but high variance, and the heterogeneity between the trees is supposed to squash the variance with the bagging. The introduction mentions that the individual trees in the forest are *less biased*. Maybe this is because of our different definitions of bias but any clarification here would be helpful.
assistant
The paper proposes a scheme to learn representations and a random forest model in a closely coupled manner, where the representations are learned to simultaneously improve the predictive performance of the individual trees in the random forest and reduce the correlation between these trees. This is achieved via a novel triplet sampling scheme where the positive samples are used to improve the predictive performance of the individual trees, while the negative samples are used to reduce the correlation between the trees. The empirical results indicate the improvement of the proposed scheme over random forests learned over the original representations across various datasets. The manuscript is well presented and I am leaning towards accept, one major issue with this manuscript is the number of pages which crosses the 8 page recommended limit in the ICLR CfP. Given that higher standard, I feel that there are multiple minor issues which should be addressed. - It is not clear why the proposed scheme is especially well suited for *domain generalization* and *visual recognition* and not for general supervised classification tasks (like those on CIFAR, SVHN, etc). This needs to be clarified. - The theoretical results for random forest rely on the independence of the individual trees. Given that the trees in the random forest are no longer independent in the proposed scheme, the upper bound in Equation (1) may no longer be valid. While this does not affect the empirical performance, it might be good to discuss the theoretical implications of the proposed scheme. - It is not clear why the curves for the canonical features in Figure 3 not improving with the number of iterations (which corresponds to number of trees to the best of my understanding). The results in Table 1 do indicate improvement in the performance with canonical features, putting Figure 3 and Table 1 at odds. Minor: - The individual trees in a random forest are supposed to have low bias but high variance, and the heterogeneity between the trees is supposed to squash the variance with the bagging. The introduction mentions that the individual trees in the forest are *less biased*. Maybe this is because of our different definitions of bias but any clarification here would be helpful.