Summary and Contributions: This paper discusses robustness to input manipulations in neural nets from a causal point of view. From this perspective, the authors motivate the design of a conditional latent variable model, whose latent space is explicitly decomposed into two parts, one is supposed to encode underlying (nuisance) factors that can be intervened, and the other one is intended to represent the remaining factors, which cannot be manipulated. A fine-tuning method on test data is further proposed to improve robustness to unseen manipulations. Empirical results show that the proposed model achieves the desired disentanglement in the latent space, and it is more robust compared to a deep neural net baseline.
Strengths: Building robust classifiers to input manipulations is an important topic. The proposed method is technically sound, and its benefits as compared to a discriminative deep neural net baseline are supported by numerical experiments.
Weaknesses: The technical contribution of this paper is marginal. The proposed model is a form of conditional variational autoencoder (CVAE) [1, 2]. Perhaps one difference with some existing CVAE variants (e.g., p(z,x,y) = p(z)p(y)p(x|z,y)) is the explicit decomposition of the latent representation into two parts Z, M, with the latter encoding nuisance factors, which can be intervened, and the former representing the remaining factors, which cannot be intervened. However, it is not clear (without comparisons with CVAE) if such decomposition is necessary to achieve the desired robustness, since VAE models can be good at learning disentangled representations. Moreover, the causal treatment and the introduction of the “do” notation do not seem to be necessary, making the positioning of this work from the causal perspective a bit weak. Experiments are weak in that the proposed model is compared to a relatively weak baseline, namely a discriminative deep neural net. Stronger baselines should be considered (e.g., Conditional Variational Autoencoder, Deep Variational Information Bottleneck (IB) ) to assess the importance of the proposed model. Some relevant references are missing [3, 4].  Sohn, Kihyuk, Honglak Lee, and Xinchen Yan. "Learning structured output representation using deep conditional generative models." NeurIPS. 2015.  Siddharth, Narayanaswamy, et al. "Learning disentangled representations with semi-supervised deep generative models." NeurIPS 2017.  Alemi, Alexander A., et al. "Deep variational information bottleneck." ICLR (2017).  Moyer, Daniel, et al. "Invariant representations without adversarial training." NeurIPS. 2018.
Correctness: Yes to the extent that I checked.
Clarity: The writing of the paper is fine.
Relation to Prior Work: Relation to conditional variational autoencoders can be improved. Please refer to the weaknesses section for more details.
Additional Feedback: Under the current objective, the latent variable Z may still encode some nuisance information, which can be intervened. It would be interesting to consider encouraging Z and M to be independent explicitly. Typo: Is significantly more robustness *** Post Rebuttal Update *** I acknowledge having read the authors rebuttal as well as the other reviews, and I decided to keep my original assessment unchanged for the following reasons. The positioning of the paper from the causality perspective is weak, the introduction of the “do” notation seems unnecessary and can be replaced by the usual observational conditioning. The model derived in 3.1 is a conditional VAE (CVAE) with a decomposed latent space. While the authors also proposes an interesting fine-tuning method, and extend their model to the multimodal case, the model of section 3.1 is central in this work. I would therefore recommend providing theoretical/empirical justification on the importance of the decomposition of the CVAE’s latent space, which I suspect may not be necessary to achieve the desired robustness. The only considered baseline is relatively weak in that it is deterministic while the proposed model is probabilistic. The rebuttal includes some results of CVAE and DVIB (Deep Variational Information Bottleck). While on the PGD attacks the proposed method seems to outperform the above two baselines, on “the vertical shift range” the results of CAVE, DVIB and the proposed CAMA are very tight, justifying the importance of considering stronger baselines. Moreover, I would recommend applying the proposed fine-tuning procedure to CVAE and DVIB for more fair comparisons.
Summary and Contributions: This paper proposes a causal approach to building latent representations of data robust to manipulations unrelated to the downstream task. The proposed method takes advantage of the knowledge of manipulations present in training to various degrees. Furthermore, their method supports fine-tuning at test time to adapt to unseen manipulations during training.
Strengths: The goal of this paper is ambitious and steps beyond the assumptions made with traditional statistical learning methods. In particular, the ability to make correct predictions on out of distribution data. The causal graph proposed in this work assumes independence between the downstream task and the manipulation, which, despite being very simplistic, also seems to be very reasonable. In general, manipulations, are not going to impact the prediction label by definition. Through experiments, the authors demonstrate improved robustness in out of distribution shifts for MNIST and adversarial attacks. Importantly, this is without explicit knowledge of the attacks or domain shifts. The manipulation prior proposed in this work seems very general and could apply broadly to more datasets than methods that explicitly address known dataset shifts.
Weaknesses: Since the authors demonstrate robustness in a non-standard domain (which is a good thing), the authors should do a better job of presenting what exactly must be known about the given dataset during training compared to a standard dataset. While exact knowledge of the domain shift is unknown beforehand, there is the knowledge that manipulation has occurred. For example, what is the exact format of the training data in line 205? Are corresponding pairs of manipulated and manipulated inputs given? Or is it just the binary label distinguishing whether the data came from the manipulated set or clean set? Equation 5 would imply the latter, but this needs to be clarified because the introduction (lines 36-37, 41-43) describes shortcoming of standard DNNs for unseen causes. However, this model does not appear to address this issue without having access to additional information. Such a clarification would help readers understand what types of data this method is suitable for and highlight how this method leverages this additional information.
Correctness: The claims appear correct, though this reviewer is not familiar with causal graphical models.
Clarity: Aside from the ambiguity of what an unseen manipulation means in the context of the introduction compared to the training datasets used in the experiments, the paper is well-written.
Relation to Prior Work: This work does mention how the method considers unseen manipulations, whereas past work does not. As mentioned in an earlier section of this review, this needs to be clarified better to distinguish it more precisely from work like: Arjovsky, Martin, et al. "Invariant risk minimization." arXiv preprint arXiv:1907.02893 (2019). which is mentioned in their related work as not considered 'unseen' manipulations. The level of knowledge of the IRM approach appears comparable to the method proposed here. IRM only requires knowledge that manipulation has occurred, not information about the specific manipulation.
Additional Feedback: ----After Rebuttal---- After going through the rebuttal and other reviews, I will keep my current score of this review. My main concern about how much knowledge of the manipulation is presented to the model during training is addressed the rebuttal. But I would suggest the authors make this knowledge very clear in future revisions. This will be a point of comparison and contrast with other work. Furthermore, fine-tuning at test time paradigm to handle out of distribution data is an interesting paradigm that I hope to see become more common.
Summary and Contributions: The paper proposes an inference framework, deep CAMA, that takes into account the causal relationship between the data and effect variables. In the framework, an encoder and generative model learn the relationships that link data samples (X) to labels (Y) and effect variables (M and Z) where M can be changed by a malicious party during adversarial attacks. During inference, the model parameters can be finetuned to learn unseen M intervention to produce robust Y predictions. The approach is interesting, especially the finetuning phase which is novel to the best of my knowledge. Though the experiments are somewhat limited to small-scaled tasks here and the adversarial robustness performance is still far from the best defenses, this work may inspire more future defenses which infer inputs with a causal view. One main concern I have is regarding the scalability of the approach versus other defenses such as adversarially trained discriminative models. Also, for the cases of horizontal/vertical shift, I am guessing the discriminative model could be robust to both types of shift by training on both horizontal/vertical shifted data. It would be more compelling if deep CAMA can outperform the discriminative model trained with this data augmentation scheme. ========================================= I acknowledge that I read the rebuttal and thank the authors for addressing the questions and concerns I had.
Strengths: - The paper shows that Deep CAMA can confer robustness to some unseen perturbations such as translational shift and may inspire future defenses taking the causal approach to robustness. - The proposed finetuning phase to learn unseen M intervention is novel and sets this paper apart from previous work like Narayanaswamy et al.
Weaknesses: - Experiments are mostly conducted on toy tasks - Unsupported and imprecise statements in the paper. (see more on clarity below)
Clarity: The paper is generally easy to follow but has unsupported/exaggerated statements such as: - Line 52: “They simply trust the observed data and ignore the constraints of the data generating process, which leads to overfiting to nuisance factors that do not cause the ground truth labels." Also, important details such as how Deep CAMA predicts its outputs given input data are not clearly stated in the paper.
Relation to Prior Work: Yes, the proposed finetuning phase to learn unseen M intervention is novel and sets this paper apart from previous work like Narayanaswamy et al.
Additional Feedback: - What is the total parameter size of Deep CAMA versus the discriminative models in your experiments? - Improve clarity in how Deep CAMA infer input data, e.g. how does the Monte Carlo approximation to Bayes’ rule work? minor edits: Line 49: “is significantly more robustness to unseen manipulations.”, robustness>robust
Summary and Contributions: This paper (A Causal View on Robustness of Neural Networks) defines a model for causal modeling of manipulations, for improving robustness of decision making methods. The importance of introducing causality framing into this problem is shown, a new mdeol (deep CAMA) is introduced, and a number of experiments are used to show the benefits CAMA can provide.
Strengths: The proposed model, while fairly involved, manages to both explain the core design principles, and necessary background to understand the design, as well as the functionality of the introduced CAMA method. Doing so in a limited space is not easy, and I commend the authors on the description of their approach as well as the necessary background. The experimental section, while performed on limited datasets, is thorough in the types of experiments performed and the breadth of use of the data itself. The inclusion of reproduction code for the model and experiments in the supplementary material is good, and I strongly encourage the authors to release it publicly upon (potential) publication. Doing so will allow many researchers to deeper understand and extend on the work shown here.
Weaknesses: Demonstrating the method on more involved image datasets than MNIST / CIFAR-10, other image tasks, or in more domains (audio separation, dereverberation, and other audio scene tasks are a place where causal knowledge could potentially boost performance) would be beneficial. However, the limited datasets are countered by multiple detailed experiments upon these datasets, so this is not a huge detraction from the paper, especially given the core contribution of the model itself. Describing roughly the practical cost of CAMA (in terms of computation) would give an idea of the robustness/computational performance tradeoff for some of these tasks, which would be useful information for practical usage.
Correctness: The derivation and core concepts of CAMA appear to be correct. The experimental methodology taken to test various aspects of deep CAMA is well-motivated, and the experiments show the benefits of using the model compared to baselines without using CAMA.
Clarity: The paper is well written, especially given that it introduces a fairly complex model, using a number of tools from causal modeling, and manages to succinctly define the model itself, the tools necessary to understand the model design, and a number of experiments within the page limit.
Relation to Prior Work: A number of related works on adversarial robustness are described, and how CAMA "fits in" to the larger picture of models attempting to handle systematic generalization and adversarial robustness is clear given background and reference material.
Additional Feedback: Given fundamental limits of network robustness to adversarial attacks (see "Limitations of Adversarial Robustness: Strong No Free Lunch Theorem"), where does the proposed method differ, or relate to that general framework for robustness / adversaries? Does the causality framework provide a "way out" from the bounds and limits shown in that work? The lack of robustness to horizontal and vertical shift in the MNIST example seem as coupled to the architectural bias of the particular discriminator design, as to the task itself - for example an object detection framework such as RCNN or modern variants (ala Mask-RCNN) should have little issue with the shifted image task described in the paper. How can we separate the issue of network design (which is frequently driven by known invariances in the desired domain - such as moving from simple DNNs to more applicable CNNs) and the causal manipulation model (which also has design parameters and potential pitfalls, as discussed in 3.2 and 4.2). If using some kind of automated network design setting (such as meta-learning or evolutionary approaches) would both the CAMA model design, and the discriminator itself need to be designed in conjunction, or some kind of back-and-forth iteration? Does knowing something about the CAMA model potentially tell you something about the ideal design of the classifier as well? Just before section 5, the statement "deep CAMA remains to be more robust than the discriminative DNN when the mis-specification is not too severe". Are there measures to determine mis-specification post-hoc? Are there guidelines or principles for model design to limit or avoid mis-specification in practice? After feedback: Thank you to the authors for descriptions and clarifications in the feedback document, and for clarifying the questions I raised during the review. The comments and discussion with regards to all reviewers should further strengthen this paper, and the discussion was very helpful.