Title: RenderGAN: Generating Realistic Labeled Data
Authors: Leon Sixt
Benjamin Wild
Tim Landgraf
Venue: ICLR 2017
Keywords: Unsupervised Learning
Computer vision
Deep learning
Applications

Abstract: Deep Convolutional Neuronal Networks (DCNNs) are showing remarkable performance on many computer vision tasks.  Due to their large parameter space, they require many labeled samples when trained in a supervised setting. The costs of annotating data manually can render the use of DCNNs infeasible.  We present a novel framework called RenderGAN that can generate large amounts of realistic, labeled images by combining a 3D model and the Generative Adversarial Network framework. In our approach, image augmentations (e.g. lighting, background, and detail) are learned from unlabeled data such that the generated images are strikingly realistic while preserving the labels known from the 3D model.  We apply the RenderGAN framework to generate images of barcode-like markers that are attached to honeybees. Training a DCNN on data generated by the RenderGAN yields considerably better performance than training it on various baselines. 

TL;DR: We embed a 3D model in the GAN framework to generate realistic, labeled data.

Review discussion:
Hello,
An interesting idea! A couple of questions:
1. Would it make sense to use a differentiable renderer (like OpenDR by Loper & Black) in your framework ?
2. Have you tried a baseline approach to synthetic data generation with simple parametric distributions of orientations and augmentations, hand-designed or estimated with simple statistics? Basically, do you really need a GAN to learn them in this relatively simple scenario, or can a human quickly make up a reasonable distribution of transformations which will perform as well ?
3. Do you think your work is related to Style and Structure Adversarial Networks https://arxiv.org/pdf/1603.05631.pdf , Spatial Transformers https://arxiv.org/abs/1506.02025 ?
4. Have you had a chance to run experiments on other datasets?
Thanks.
> 1. Would it make sense to use a differentiable renderer (like OpenDR by Loper & Black) in your framework ?
Thanks for the suggestion. Yes, it would make sense. In future work, this could make it possible to partially infer the 3D model from data.
> 2. Have you tried a baseline approach to synthetic data generation with simple parametric distributions of orientations and augmentations, hand-designed or estimated with simple statistics? Basically, do you really need a GAN to  learn them in this relatively simple scenario, or can a human quickly make up   a reasonable distribution of transformations which will perform as well?
We initially tried a very basic approach to augment the 3D model data with only noise and shifting of the pixel intensities. A model trained on this data did not generalize to real data.
Based on your suggestion, we implemented hand designed augmentations similar to the ones learned by the GAN. Preliminary results indicate a network trained on this data overfits much more than when trained on the GAN data. We will update the paper shortly.
> 3. Do you think your work is related to Style and Structure Adversarial Networks
https://arxiv.org/pdf/1603.05631.pdf , Spatial Transformers
https://arxiv.org/abs/1506.02025 ?
In the Style and Structure Adversarial Networks paper, a GAN is adapted to not only generate
images but also to capture the structure representation in the form of a normal map.
Contrary to our work, their training procedure requires large amounts of labeled data.
They rely on 200K normal maps and matching real images from
the NYU Depth Dataset V2.
Still, Wang and Gupta successfully learn to separate
style from structure. Something we are also able to achieve with the RenderGAN
framework. The 3D model would be the structure and the augmentations the style.
We think this work is related to our work and we should mention it.
Spatial Transformers (STs) learn an affine transformation of
convolutional feature maps. It was shown that this can improve the performance
on rotated or otherwise deformed data.
Our approach is only related to STs such that STs could
be used as an augmentation function.
For example, in our application the 3D model directly incorporates
translation and rotation, it should instead be possible to learn them with a ST.
In future work, ST could help to reduce the complexity of the 3D model.
> 4. Have you had a chance to run experiments on other datasets?
Currently, we are investigating to apply the RenderGAN framework on a different
dataset but we have no results yet.
- the proposed method is more model driven that previous GAN models.
But does it pay off? how would a traditional GAN approach perform? The mentioned effects like blur, lighting and background could also potentially be modelled by upsamling network that directly predicts the image. I would assume that blur and lighting can be modelled by convolutions. transformations to some extend by convolutions - or spatial transformer networks.
- how does it compare to traditional data augmentation techniques, e.g. noise, dropout, transformations. you are linking to keras code - where data augmentation is readily available and could be tested (ImageDataGenerator)
- How do the different stages (\phis) effect performance? which are the most important ones?
> the proposed method is more model driven that previous GAN models.
But does it pay off? how would a traditional GAN approach perform? The mentioned effects like blur, lighting and background could also potentially be modelled by upsamling network that directly predicts the image. I would assume that blur and lighting can be modelled by convolutions. transformations to some extend by convolutions - or spatial transformer networks.
The payoff is that we can generate labeled data with only a simple 3D model
and unlabeled data. You are right. A DCGAN architecture can model all mentioned
effects, even affine transformations. We trained a DCGAN on the data, and
the quality of the synthesized images is similar.
However, no labels can be collected in the conventional GAN framework. The latent space is too entangled, i.e. there is no simple relationship of the labels to the latent space. A conditional GAN can be used to generated labeled data but also requires labeled data for training.
> how does it compare to traditional data augmentation techniques, e.g. noise, dropout, transformations. you are linking to keras code - where data augmentation is readily available and could be tested (ImageDataGenerator)
Is your question about augmenting the images of the 3D model or the real data?
For the 3D model, see our answer to question 2. from reviewer number 1.
The real data that is used for training is highly augmented. This is mentioned in the result section (random translations, rotations, shear transformations, histogram scaling, and noise). We will include the specific parameters used to augment the data in the appendix.
> How do the different stages (\phis) effect performance? Which are the most important ones?
We agree that this should be addressed in the paper. We are currently evaluating the effects of the different stages on the network performance by replacing learned augmentations (\phis) with hand-designed augmentations (see also our reply to reviewer 1 / question 2). We will update the paper shortly to include this evaluation.
We updated our paper based on the feedback from the pre-review questions.  We
included handmade augmentation in the evaluation.  We also retrained the DCNN on
the real data. Thanks for the feedback.
The submission proposes an interesting way to match synthetic data to real data in a GAN type architecture.
The main novelty are parametric modules that emulate different transformations and artefact that allow to match the natural appearance.
several points were raised during the discussion:
1. the proposed method is more model driven that previous GAN models.
But does it pay off? how would a traditional GAN approach perform? The mentioned effects like blur, lighting and background could also potentially be modelled by upsamling network that directly predicts the image. I would assume that blur and lighting can be modelled by convolutions. transformations to some extend by convolutions - or spatial transformer networks.
The answers of the authors only partially addresses the point. The key proposal of the submission seems parameterised modules that can be trained to match the real data distribution.
but it remains unclear why not a more generic parameterisation can also do the job. E.g. a neural network - as done in regular GANs. The benefit of introducing a stronger model is unclear. Using a render engine to generate the initial sample appearance if of limited novelty.
2. how does it compare to traditional data augmentation techniques, e.g. noise, dropout, transformations. you are linking to keras code - where data augmentation is readily available and could be tested (ImageDataGenerator)
The authors reply that plenty of such augmentation was used and more details are going to be provided in the appendix. it would have been appreciated if such information was directly included in the revision - so that the procedure could be directly checked. right now - this remains a point of uncertainty.
3. How do the different stages (\phis) effect performance? which are the most important ones?
The authors do evaluate the effect of hand tuning the transformation stages vs. learning them. it would be great to also include results of including/excluding stages completely - and also reporting how much the initial jittering of the data helps.
While there is an interesting idea of (limited) novelty to the paper, there are some concerns about evalations and comparisons as outlined above.
In addition, only success on a single dataset/task is shown.
Yet the task is interesting and seems challenging.
Overall, this remains makes only a weak recommendation for acceptance.
Thank you very much for your review. Your pre-review questions already helped to
improve the paper.
However, we disagree with several points raised in your
review.
> The main novelty are parametric modules that emulate different transformations
> and artefact that allow to match the natural appearance.  [...] the proposed
> method is more model driven that previous GAN models.
But does it pay off? how
> would a traditional GAN approach perform?  [...] The key proposal of the
> submission seems parameterised modules that can be trained to match the real
> data distribution.
but it remains unclear why not a more generic
> parameterisation can also do the job. E.g. a neural network - as done in regular
> GANs.
We disagree with your summary that our main contribution are augmentation
functions that allow a GAN to learn the real data distribution.
In fact, the
augmentation functions are specifically designed to constrain the generative
capabilities of the model. A generic GAN cannot be used as a baseline for our
method. Please refer to our general rebuttal for a more thorough reply.
> Using a render engine to generate the initial sample appearance if of limited novelty.
The point of using a render engine is not to generate an initial appearance but
to allow collecting a _labeled_ sample that subsequently is rendered more
realistic by a constrained GAN. To the best of our knowledge, this has never
been done before.
> The answers of the authors only partially addresses the point.
This is incorrect. We explained in our pre-review reply that a generic GAN
cannot be used as a baseline.
Furthermore, we elaborated on this point in our
general rebuttal. Could you please clarify your point if you feel that we still
didn’t address your questions completely?
> The authors reply that plenty of such augmentation was used and more details are
> going to be provided in the appendix. it would have been appreciated if such
> information was directly included in the revision - so that the procedure could
> be directly checked. right now - this remains a point of uncertainty.
It was already included in the revision from 9 Dec 2016 which is based on your
pre-review feedback (see appendix C page 14).
> The authors do evaluate the effect of hand tuning the transformation stages vs.
> learning them. it would be great to also include results of including/excluding
> stages completely
We agree, excluding different transformation stages would shed light on the
relative importance of each stage, and this would be an interesting aspect. We
will try to include this in the manuscript by the submission deadline. For now,
the differences in performance obtained by hand-designed and learned
augmentations can be used as a rough proxy for the importance of the individual
augmentations.
For example, there is a large improvement in validation
performance when replacing the hand-designed lighting augmentation with
a learned one. We added a respective remark to the latest revision (9 Dec).
> - and also reporting how much the initial jittering of the data helps.
It is not clear to us what you mean with initial jittering of the data. Would
you mind clarifying this point?
The paper proposes an approach to generating synthetic training data for deep networks, based on rendering 3D models and learning additional transformations with adversarial training. The approach is applied to generating barcode-like markers used for honeybee identification. The authors demonstrate that a classifier trained on synthetic data generated with the proposed approach outperforms both training on (limited) real data and training on data with hand-designed augmentations.
The topic of the paper — using machine learning (in particular, adversarial training) for generating realistic synthetic training data — is very interesting and important. The proposed method looks reasonable, and the paper is written well. The downside is that experiments are limited to a fairly simple and not-widely-known domain of honeybee marker classification.
While I am sure this is an important task by itself, in order to demonstrate general applicability of the method and to allow comparison with existing techniques, experiments on some standard and/or realistic datasets would be very helpful.
Overall, I recommend acceptance, but encourage the authors to perform experiments on more datasets.
I appreciate that the authors added a baseline with manually designed transformations. This strengthens the paper.
As Reviewer3 points out, it would be interesting to analyze if restricting GAN to a fixed set of transformations is necessary here, and which transformations are most important. Perhaps this would provide some guidelines for designing sets of transformations for more complicated scenarios.
The authors should tone down their claims such as “Our method is an improvement over previous work  <...> Whereas previous work relied on real data for training using pre-trained models or mixing real and generated data, we were able to train a DCNN from scratch with generated data that performed well when tested on real data. “. This is not a fair comparison: the domain studied by authors in this work is much simpler than what was studied in these previous works, so this comparison is not appropriate.
Thank you very much for your review. You summarized our work well, and your
feedback contributed significantly to improving our paper.
> As Reviewer3 points out, it would be interesting to analyze if restricting GAN
> to a fixed set of transformations is necessary here, [...].
Please see our general rebuttal for a detailed explanation why the
transformations are necessary and cannot be left out. We will revise the
manuscript to make this point clearer.
> The downside is that experiments are limited to a fairly simple and
> not-widely-known domain of honeybee marker classification.
We agree, there are more complex datasets e.g. ImageNet.
However, our problem,
i.e. extraction of the marker’s ID and rotation in space, was unsolvable with
state-of-the-art supervised learning methods due to the time-consuming nature of
manual labeling.  We are currently working on more popular problem domains. We,
however, feel that, although the problem has limited applicability for the
general reader, it nonetheless represents a complex problem with analogies to
other, potentially more popular problems, like pose estimation for cars or
faces.
> The authors should tone down their claims such as “Our method is an improvement
> over previous work  <...> Whereas previous work relied on real data for training
> using pre-trained models or mixing real and generated data, we were able to
> train a DCNN from scratch with generated data that performed well when tested on
> real data. “. This is not a fair comparison: the domain studied by authors in
> this work is much simpler than what was studied in these previous works, so this
> comparison is not appropriate.
We fully agree and will change the paragraph accordingly.
This paper addresses the problem of decoding barcode-like markers depicted in an image.  The main insight is to train a CNN from generated data produced from a GAN.  The GAN is trained using unlabeled images, and leverages a "3D model" that undergoes learnt image transformations (e.g., blur, lighting, background).  The parameters for the image transformations are trained such that it confuses a GAN discriminator.  A CNN is trained using images generated from the GAN and compared with hand-crafted features and from training with real images.  The proposed method out-performs both baselines on decoding the barcode markers.
The proposed GAN architecture could potentially be interesting.
However, I won’t champion the paper as the evaluation could be improved.
A critical missing baseline is a comparison against a generic GAN.  Without this it’s hard to judge the benefit of the more structured GAN.
Also, it would be worth seeing the result when one combines generated and real images for the final task.
A couple of references that are relevant to this work (for object detection using rendered views of 3D shapes):
[A] Xingchao Peng, Baochen Sun, Karim Ali, Kate Saenko, Learning Deep Object Detectors from 3D Models; ICCV, 2015.
[B] Deep Exemplar 2D-3D Detection by Adapting from Real to Rendered Views. Francisco Massa, Bryan C. Russell, Mathieu Aubry. CVPR 2016.
The problem domain (decoding barcode markers on bees) is limited.  It would be great to see this applied to another problem domain, e.g., object detection from 3D models as shown in paper reference [A], where direct comparison against prior work could be performed.
I found the writing to be somewhat vague throughout.
For instance, on first reading of the introduction it is not clear what exactly is the contribution of the paper.
Minor comments:
Fig 3 - Are these really renders from a 3D model?  The images look like 2D images, perhaps spatially warped via a homography.
Page 3: "chapter" => "section".
In Table 2, what is the loss used for the DCNN?
Fig 9 (a) - The last four images look like they have strange artifacts. Can you explain these?
Thank you very much for your review. Your comments have helped to improve the
paper.  We think your main point of critique was that we did not evaluate our
system sufficiently:
> “However, I won’t champion the paper as the evaluation could be improved.
> A critical missing baseline is a comparison against a generic GAN. Without this
> it’s hard to judge the benefit of the more structured GAN. “
Since similar comments were made by the other reviewers, we formulated a general
response to this point. Please refer to the “general rebuttal” in which we give
a detailed rationale of why a generic GAN cannot be used as a baseline as you
(and others) suggested. We think this is a crucial point representing the
central motivation of the RenderGAN method.
> “Also, it would be worth seeing the result when one combines generated and
>  real images for the final task.”
In previous experiments, we used a mixture of real vs. generated data (5/95 and
50/50). The real data was augmented to prevent overfitting as described in
Appendix C in the paper. Both experiments did not result in significant
improvements, and we therefore decided not to include them in the paper. We
agree that this might be interesting and we will update the manuscript
accordingly.
Thank you for pointing out additional references. We will include both of them in our next update.
> “The problem domain (decoding barcode markers on bees) is limited.  It would
> be great to see this applied to another problem domain, e.g., object detection
> from 3D models as shown in paper reference [A], where direct comparison against
> prior work could be performed. “
We are currently working on more popular problem domains. We, however, feel
that, although the problem has limited applicability for the general reader, it
nonetheless represents a complex problem with analogies to other, potentially
more popular problems, like e.g. pose estimation for cars or faces.
> “I found the writing to be somewhat vague throughout.
For instance, on first
> reading of the introduction it is not clear what exactly is the contribution of
> the paper.”
Thank you for this feedback. We are currently revising the manuscript to improve
overall clarity and will update the paper in the following days.
Thank you also for your minor comments, the manuscript will be updated with
appropriate changes.
> “Fig 3 - Are these really renders from a 3D model? The images look like 2D
> images, perhaps spatially warped via a homography.”
Yes, the bee marker is represented by a 3D mesh which is rotated in 3D space
according to the parameters and then projected to the image plane. Here is
a link to the code (https://goo.gl/9UWuhi). This is a very simple 3D model and
we believe this is one of the advantages of the RenderGAN method. Only
a rudimentary object and camera model are required. All the rest of the imaging
process, lighting, shadows, blur, reflections, compression artifacts, etc. is
learned by the RenderGAN.
> “Page 3: "chapter" => "section".”
Changed accordingly.
> “In Table 2, what is the loss used for the DCNN?”
The loss is the mean log-loss for the classification of the individual bits of
the barcode marker. We will include this in the caption of the figure in the
next revision.
> “Fig 9 (a) - The last four images look like they have strange artifacts.
> Can you explain these?”
We mentioned them briefly in the text (Section 3, Training):
> In some cases, the generator creates artifacts that destroy the images. As
> these bad images are scored unrealistic by the discriminator without exception,
> we can discard them for the training of the supervised algorithm.
We suspect that during training the generator can deceive the discriminator by
producing out of distribution samples i.e. very unrealistic images. The
discriminator learns quickly to score them as fake, and now the gradient for the
generator might be too small to escape this local minimum.
However, this is only
a hypothesis which we did not investigate further.  The images with the strange
artifacts are rated unrealistic by the discriminator without exception.
Therefore, we can sort them out before training the DCNN.
Thank you very much for your reviews. Your feedback helped to improve the
manuscript significantly, and we are preparing a revised version of the
manuscript with changes outlined either below or in our responses to each
reviewer. Multiple valid points of criticism were raised during the review
process and have already been worked into the current version of the document.
For example, we included hand-designed augmentations for comparison with the
learned ones.
However, in two of the three reviews there seems to be a major misunderstanding
that we would like to clarify here. Since this relates to the central finding of
our paper, we would like to provide a detailed response to this point. We hope
that, in the light of this fact, the reviewer’s rating of our contribution’s
importance and novelty will be reconsidered.
> Reviewer 2: “A critical missing baseline is a comparison against a generic GAN.
> Without this it’s hard to judge the benefit of the more structured GAN.
Also,
> it would be worth seeing the result when one combines generated and real images
> for the final task.”
> Reviewer 3: “ [...] the proposed method is more model driven that previous GAN
> models.
But does it pay off? how would a traditional GAN approach perform? [...]
> The answers of the authors only partially addresses the point. The key proposal
> of the submission seems parameterised modules that can be trained to match the
> real data distribution.
but it remains unclear why not a more generic
> parameterisation can also do the job. E.g. a neural network - as done in regular
> GANs. The benefit of introducing a stronger model is unclear.”
The main point of critique here is that a comparison with a generic GAN
(Goodfellow et al. 2014) is missing. This comment implies that both methods (GAN
and RenderGAN) share the same task domain, which is incorrect. The task we
address is generating _labeled_ data. We emphasize that we do not refer to the
binary class label but rather to higher dimensional labels. In our example
scenario, this corresponds to images of bee markers and their respective bit
configuration (its ID) and rotations in 3D space. A generic GAN cannot generate
labels, it learns to generate realistic images _without_ labels.
Ultimately, we
want to train a convnet (‘decoder network’) in a supervised setting to map an
image to its respective labels.
Thus, we need labeled samples and hence,
a conventional GAN cannot be used as a baseline! Stated formally: the RenderGAN
samples from the joint distribution p(l, x) of labels l and data x whereas
a conventional GAN can only sample from the data distribution p(x).
There are two alternative approaches to our RenderGAN, one being a conventional
3-dimensional rendering pipeline that can be used to generate images of bee
markers with known ID and spatial orientation.
Secondly, one could train the
decoder network with manually labeled data. Both approaches have been
implemented and tested against the RenderGAN and do not perform satisfyingly. To
improve both alternatives’ performance one would need to either tune the
rendering pipeline to match the details of the real world imaging process, or
label more data manually. Both measures are time-consuming and do not generalize
well when changing parts of the imaging process (lighting, cameras, compression,
etc.) or the marker design.
Our approach is to extend a generic GAN by adding several network modules, the
first being the network equivalent of a simple 3D model.
Secondly, we learn
a number of parameterised augmentation functions. We would like to point out
that this approach was _not_ chosen to improve the generative capabilities of
the network but to constrain it in such a way that the image produced by the GAN
is correct with respect to the labels fed into the network. In our use case,
each image produced by the GAN has to preserve the given bit pattern and
rotation in space provided by the 3D model for the labels to remain valid. This
point was already addressed in our paper and the pre-review questions:
> Paper Introduction: “[...] We constrain the augmentation of the images such that
> the high-level information represented by the 3D model is preserved. The
> RenderGAN framework allows us to generate images of which the labels are known
> from the 3D model, and that also look strikingly real due to the GAN framework.
> The training procedure of the RenderGAN framework does not require any labels.
> We can generate high-quality, labeled data with a simple 3D model and a large
> amount of unlabeled data.”
> Our reply to Reviewer 3: “[...] The payoff is that we can generate labeled data
> with only a simple 3D model and unlabeled data. You are right. A DCGAN
> architecture can model all mentioned effects, even affine transformations. We
> trained a DCGAN on the data, and the quality of the synthesized images is
> similar.
However, no labels can be collected in the conventional GAN framework.
All reviewers question the necessity of the constraints we introduced. One of
our early approaches was to add an offset to the 3D model, i.e. x = t + g(t)
where x is the synthesized image, t is an image from the 3D model, and g an
unconstrained generator.
However, in our experiments, the generator learned to
synthesize realistic images but ignored the given template t completely.
Thus,
no valid labels of the synthetic images could be collected. Since a decoder
network cannot be trained without labels, this approach cannot be used as
a baseline. We will revise our paper to clarify that an unconstrained GAN is not
a suitable baseline for our task.
Could a generic GAN indeed not be applied? One could train a conditional GAN conditioned on the ID, same way as class-conditional GANs are trained, that is, for example, by feeding the ID both to the generator and the discriminator. I would expect this to fail horribly, but it might in principle work. One would need labeled data to train it, though.
Alternatively, in a more realistic scenario, one might keep the "rendering" part, but replace everything else by a generic network which takes this clean rendered image and some noise as input, and aims to generate a realistic marker image from that. This might not even require labeled data.
Overall, I think these are somewhat reasonable baselines, and they would make the paper more complete, but I would not expect them to work well, or at least they would be much harder to tune.
Thank you for your response. You proposed two baselines, a cGAN version and an
unconstrained GAN. As we previously explained (see general rebuttal letter),
these are no viable or credible baselines.
As you mentioned correctly, the cGAN approach requires labeled data. We do not
have such a dataset. Annotating enough data for this approach to represent
a credible baseline would be prohibitively expensive. Remember that we have
multiple labels such as 3D orientation and 12 binary labels for each sample.
Annotating one sample can easily take 2 minutes. If we would annotate 20 samples
for every possible ID, it would take a person 341 days. If we would have this
kind of labeled data, we would directly use it to train the decoder network. The
scarcity of labeled data is the main motivation for our approach and we think
there exist many similar problems that can be solved with the RenderGAN
approach.
Secondly, you proposed an “unconstrained” generator network as a baseline. We
argue in our rebuttal:
> One of our early approaches was to add an offset to the 3D model, i.e.
> x = t + g(t) where x is the synthesized image, t is an image from the 3D model,
> and g an unconstrained generator.
However, in our experiments, the generator
> learned to synthesize realistic images but ignored the given template
> t completely.
Thus, no valid labels of the synthetic images could be collected.
Again, we cannot use a generic GAN to add missing image characteristics. This
intuitive notion is incorrect. The GAN generates images that deviate from the
input labels. The images might display a different ID or a different spatial
orientation compared to the input labels. Our experiments then led us to the
idea of introducing constrained augmentations that ensure that the labels remain
valid in the output image. This is the central idea of the paper and as far as
we see a straight-forward solution to the problem stated above.
We uploaded a new version of the paper based on the peer review feedback.
* Clarified that an unconstrained GAN is not a suitable baseline.
* Added additional references pointed out by reviewer 2.
* Extracted a related work section to improve the overall clarity and also stated our contribution explicitly.
* Various modifications to improve clarity.

Meta-review:
role: PC
data: comment: This paper was fairly well received by the reviewers in terms of the underling idea but the fact that a very specialized problem was the focus of the paper held back reviewers from giving stronger ratings. The question of what sorts of baselines would be reasonable was discussed extensively as the reviewers felt that other credible baselines should be included. The authors argue certain baselines are not appropriate but they were not able to clearly sway the reviewers to a more positive rating based on their response to this issue. We recommend a workshop invitation for this paper.

Decision: Invite to Workshop Track