Venue: ICLR 2020 Conference

Reviews:

final_score: rating: 6: Weak Accept
review_content: The paper aims to improve the random forest performance by iterative constructing & feeding more powerful features that are to be used by the random forest learning processing where a random subset of features are chosen from the current feature pool when making growing/stopping decision at the current split node. The idea is new and interesting, and its usefulness has been empirically shown. On the other hand, it is not clear how this additional procedure would do with the good properties of RFs such as less subjective to overfitting and bias. It would be very helpful if the paper could shred some lights in this regard.

review_title: Official Blind Review #2

final_score: rating: 3: Weak Reject
review_content: This paper proposes a method for generalized image recognition based on random forest, that use directly the features extracted by the backbone, that assign pseudo-labels to the data. The learning is performed with a triplet loss adapted for better generalization.
Decision: weak reject
Motivation: the method is incremental and presented in general in a clear way and easy to follow, the authors present a simple but interesting trick to make the triplet loss more effective on a random forest in the case of generalization to a new unlabeled dataset. This method looks incremental to me because it is addressing the problem of pseudo-labelling for learning on a new dataset and instead of using confidence measures uses a random forest to assign labels. 
The experimental section of the paper is a bit confusing because is not clear if the results presented are with comparable network (e.g. ResNet18) like the cited state-of-the-art papers, from further readings I am confident the autors compared fairly with similar architectures. Authors should perhaps stress they compare with state-of-the-art in fair condition to avoid confusion as in my case. How much is the overhead of building the random forest for each iteration of the learning (algorithm 1), a more detailed analysis on this is useful for understanding the method. Could this method be used to train a network from scratch on an unlabeled data or on data with noisy labels? How did the authors choose the T decision trees, is there any ablation study, general practice or euristics behind the choice of 1,10,50? The comparison with state-of-the-art Tab 3 and Tab 4 shows that for some datasets other techniques are better, did the authors draw some conclusions from that? Comparing Tab 3 and 4 with Tab 5/6/7/8/9 looks like this method can work but only in the case of much bigger network like ResNet50 and DenseNet 161 which can limit its use for high resources (computing power) cases.
Replicability: I think with improvements in the experimental section the method results can be replicated. At the moment it lack many details like learning rates, epoch of training and other useful information that are useful.
Minor: there are two lines out of the 9 page limit
review_title: Official Blind Review #3

final_score: rating: 6: Weak Accept
review_content: 
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.

review_title: Official Blind Review #1

Meta-review: The authors introduce an approach to learn a random forest model and a representation simultaneously. The basic idea is to modify the representation so that subsequent trees in the random forest are less correlated.  The authors evaluate the technique empirically and show some modest gains. While the reviews were mixed, the approach is quite different from the usual approaches published at ICLR  and so I think it's worth highlighting this work.

Decision: Accept (Poster)