Venue: ICLR 2020 Conference

Reviews:

final_score: rating: 3: Weak Reject
review_content: This paper proposed a piecewise linear close form expression for the Stein’s unbiased risk estimator and use this formulation to construct a new Encoder-decoder convolutional neural network. The author claimed that this closely related to bagging. Improved experimental results on two inverse problems are presented. Overall, the experiment results are encouraging but the paper need clarification on a few points.

1. In the model description part, the intuition behind the attention modules is never mentioned. It will be nice to explain the intuition and possibly attached the derivation of the loss function the attention modules. 

2. the author seems misunderstand the difference between boosting and bagging. The way described in the paper is bagging and in order to do boosting, a sequential type of network structure probably need to be proposed.

3. How will be model performance compared with a simple bagging for the baseline compared in the experiment part?

review_title: Official Blind Review #2

final_score: rating: 1: Reject
review_content: Summary: The authors consider an encoder decoder setup for linear deblurring problem and propose efficient boosting estimators. Specifically, they use the Stein's unbiased risk estimator for the problem when the noise is gaussian. In the case when the encoder and decoder is represented by a convolutional neural network with RELU activations, they show how they can exploit the recent theoretical results that show the kernel type results to make their procedure efficient. They then propose using a set of models (boosting) and prove that the boosted loss function lower bounds the "nonboosted" loss function.

1. I think Proposition 1 has minor errors, there is no need to apply Jensen's inequality since there's nothing random, but I think the claim is correct -- it is trivial. In experiments, they use attention network which is not a CNN, so I'm not sure how any of the theory applies to this case, can you please clarify?

2. Experimental focus of the paper is to analyze biomedical datasets -- HCP, EDX and the authors compared their method to *only* one baseline. I suggest that they perform some more comparisons on natural images like http://vllab.ucmerced.edu/wlai24/cvpr16_deblur_study/
review_title: Official Blind Review #1

final_score: rating: 6: Weak Accept
review_content: 1. Summary
The authors address the problem of efficiently employing the SURE estimator as a network training regularizer. They show that for CNN autoencoders this can be efficiently computed. Their other contribution is a bagging/boosting technique which is proved to avoid trivial solutions. The proposed architecture, motivated by the theoretical statements, is shown to outperform classic and 2019 state of the art image reconstruction algorithms in MRI and EDX.
2. Decision and arguments
Unfortunately this paper is outside my expertise so I can’t evaluate the novelty of the theoretical accomplishments. However taking that as a given, they well-motive the proposed architecture and achieve impressive experimental results. The experiments are well described.
3. Questions 
a) Why do Table 1 and Figure 3 provide different PSNR and SSIM values?
b) Is there any way to measure accuracy to ground-truth with the EDX data? Or are the results just qualitative? 
c) With respect to Figure 2, and in general for autoencoders, the input and output have the same dimension. So how do you reconcile this with undersampled MRI and EDX data? I understand you train on fully sampled data—then how do you input undersampled data? Are the unknown samples set to zero?

review_title: Official Blind Review #3

Meta-review: This paper introduces a closed-form expression for the Stein’s unbiased estimator for the prediction error, and a boosting approach based on this, with empirical evaluation. While this paper is interesting, all reviewers seem to agree that more work is required before this paper can be published at ICLR.  
Decision: Reject