Paper:
Dual Swap Disentangling
Learning interpretable disentangled representations is a crucial yet challenging task. In this paper, we propose a weakly semi-supervised method, termed as Dual Swap Disentangling (DSD), for disentangling using both labeled and unlabeled data. Unlike conventional weakly supervised methods that rely on full annotations on the group of samples, we require only limited annotations on paired samples that indicate their shared attribute like the color. Our model takes the form of a dual autoencoder structure. To achieve disentangling using the labeled pairs, we follow a "encoding-swap-decoding" process, where we first swap the parts of their encodings corresponding to the shared attribute, and then decode the obtained hybrid codes to reconstruct the original input pairs. For unlabeled pairs, we follow the "encoding-swap-decoding" process twice on designated encoding parts and enforce the final outputs to approximate the input pairs. By isolating parts of the encoding and swapping them back and forth, we impose the dimension-wise modularity and portability of the encodings of the unlabeled samples, which implicitly encourages disentangling under the guidance of labeled pairs. This dual swap mechanism, tailored for semi-supervised setting, turns out to be very effective. Experiments on image datasets from a wide domain show that our model yields state-of-the-art disentangling performances.

Review:
This papers presents a model that elicits interpretable disentangled representations from weakly supervised data. It does so through a dual-autoencoder model fit for both unlabelled and labelled data, where labelled data assumes knowledge of which  part of the representation should be the shared one. The core of the model is the operation of swapping the shared part of the representation, when data is labelled, and dual-swapping of randomly selected parts of the representation in the case of unlabelled data.   Strengths - the method makes sense, and is a well-thought combination of existing approaches (swapping, dual mechanism and parts of representations as labels) - it presents a nice way to add possibly softer labels, e.g. which part of the representation should be shared, as opposed to stronger labels (without forcing semantics)  Weaknesses - the presented results do not give me the confidence to say that this approach is better than any of the other due to a lot of ad-hoc decisions in the paper (e.g. digital identity part of the code vs full code evaluation, the evaluation itself, and the choice of the knn classifier) - the results in table 1 are quite unusual - there is a big gap between the standard autoencoders and the variational methods which makes me ask whether there’s something particular about the classifier used (knn) that is a better fit for autoencoders. the particularities of the loss or the distribution used when training. why was k-nn used? a logical choice would be a more powerful method like svm or a multilayer perceptron. there is no explanation for this big gap - there is no visual comparison of what some of the baseline methods produce as disentangled representations so it’s impossibly to compare the quality of (dis) entanglement and the semantics behind each factor of variation - the degrees of freedom among features of the code seem binary in this case, therefore it is important which version of vae and beta-vae, as well as infogan are used, but the paper does not provide those details - the method presented can easily be applied on unlabeled data only, and that should have been one point of comparison to the other methods dealing with unlabeled data only - showing whether it works on par with baselines when no labels are used, but that wasn’t done. the only trace of that is in the figure 3, but that compares the dual and primary accuracy curves (for supervision of 0.0), but does not compare it with other methods - though parts of the paper are easy to understand, in whole it is difficult to get the necessary details of the model and the training procedure (without looking into the appendix which i admittedly did not do, but then, i want to understand the paper fully (without particular details like hyperparameters) from the main body of the paper). i think the paper would benefit from another writing iteration   Questions: - are the features of the code binary? because i didn’t find it clear from the paper. if so, then the effects of varying the single code feature is essentially a binary choice, right? did the baseline (beta-)vae and infogan methods use the appropriate distribution in that case? i cannot find this in the paper - is there a concrete reason why you decided to apply the model only in a single pass for labelled data, because you could have applied the dual-swap on labelled data too - how is this method applied at test time - one needs to supply two inputs? which ones? does it work when one supplies the same input for both? - 57 - multi dimension attribute encoding, does this essentially mean that the projection code is a matrix, instead of a vector, and that’s it? - 60-62 - if the dimensions are not independent, then the disentanglement is not perfect - meaning there might be correlations between specific parts of the representation. did you measure/check for that? - can you explicitly say what labels for each dataset in 4.1 are - where are they coming from? from the dataset itself? from what i understand, that’s easy for the generated datasets (and generated parts of the dataset), but what about cas-peal-r1 and mugshot? - i find the explanation in 243-245 very unclear. could you please elaborate what this exactly means  - why is it 5*3 (and not 5*2, e.g. in the case of beta-vae where there’s a mean and stdev in the code) - 247 - why was k-nn used, and not some other more elaborate classifier? what is the k, what is the distance metric used? - algorithm 1, ascending the gradient estimate? what is the training algorithm used? isn’t this employing a version of gradient descent (minimising loss)? - what is the effect of the balance parameter? it is a seemingly important parameter, but there are no results showing a sweep of that parameter, just a choice between 0.5 and 1 (and why does 0.5 work better)? - did you try beta-vae with significantly higher values of beta (a sweep between 10 and 150 would do)?  Other: - the notation (dash, double dash, dot, double dot over inputs is a bit unfortunate because it’s difficult to follow) - algorithm 1 is a bit too big to follow clearly, consider revising, and this is one of the points where it’s difficult to follow the notation clearly - figure 1, primary-stage I assume that f_\phi is as a matter of a fact two f_\phis with shared parameters. please split it otherwise the reader can think that f_\phi accepts 4 inputs (and in the dual-stage it accepts only 2) - figure 3 a and b are very messy / poorly designed - it is impossible to discern different lines in a because there’s too many of them, plus it’s difficult to compare values among the ones which are visible (both in a and b). log scale might be a better choice. as for the overfitting in a, from the figure, printed version, i just cannot see that overfitting - 66 - is shared - 82-83 Also, require limited weaker…sentence not clear/gramatically correct - 113 - one domain entity - 127 - is shared - 190 - conduct disentangled encodings? strange word choice - why do all citations in parentheses have a blank space between the opening parentheses and the name of the author? - 226 - despite the qualities of hybrid images are not exceptional - sentence not clear/correct - 268 - is that fig 2b or 2a? - please provide an informative caption in figure 2 (what each letter stands for)  UPDATE: I’ve read the author rebuttal, as well as all the reviews again, and in light of a good reply, I’m increasing my score.  In short, I think the results look promising on dSprites and that my questions were well answered. I still feel i) the lack of clarity is apparent in the variable length issue as all reviewers pointed to that, and that the experiments cannot give a fair comparison to other methods, given that the said vector is not disentangled itself and could account for a higher accuracy, and iii) that the paper doesn’t compare all the algorithms in an unsupervised setting (SR=0.0) where I wouldn’t necessarily expect better performance than the other models.