From: NIPS.cc/2018/Workshop/CDNNRIA
title: Acceptance Decision
decision: accept

From: NIPS.cc/2018/Workshop/CDNNRIA/Paper48/AnonReviewer1
comment: Thank you for the clarification. The score is revised.

From: NIPS.cc/2018/Workshop/CDNNRIA/Paper48/Authors
comment: Thank you for your comments.

We agree that using recurrent convolution (RC) alone can't accelerate the network. In our work, we view RC as a complementary compression strategy. One can accelerate the trained RC network by pruning and quantization, just like accelerating the standard networks.

If we want to accelerate the network using RC alone, perhaps we can apply different unrolling steps for different samples (less step for the simple samples and more step for the complex samples). Also, we need to judge whether a sample is simple or not efficiently. We leave this idea in the future work.

As mentioned above, we view RC as a compression strategy which is complementary to pruning and quantization. Thus we don't compare with the pruning or quntization methods in the current version of our paper. We will do more detailed comparisons in the future.

From: NIPS.cc/2018/Workshop/CDNNRIA/Paper48/AnonReviewer9
title: Simple but effective ideas
review: This paper proposes to use recurrent convolution for neural network compression.  The authors find that by using independent batch normalization parameters, the compression result could be improved dramatically. 

pros: (1) The idea is smiple but effective; (2) From my knowledge, using recurrent comvolution for NN compression is novel. 

cons: 
(1) It seems that the proposed method cannot accelerate the network because it has to run the same network several times. 
(2) The authors should compare their method with other compression methods, such as quntization. 


rating: 3: Marginally above acceptance threshold
confidence: 2: The reviewer is fairly confident that the evaluation is correct

From: NIPS.cc/2018/Workshop/CDNNRIA/Paper48/Authors
comment: Dear reviewers, we thank you for your helpful comments.
Now, we have updated our paper based on some of your comments.
We add the following supplementary contents into the appendix:
1) detailed network architecture.
2) detailed training setups.
3) training loss curves and test error curves on CIFAR-10 for different networks.
4) more learned parameters of BN layers.

From: NIPS.cc/2018/Workshop/CDNNRIA/Paper48/Authors
comment: Thanks a lot for your recognition of our work.
We have updated our paper.
We mention the reason for gradient clip in the main text.
We also plot training curves and more learned BN parameters on appendix C.

From: NIPS.cc/2018/Workshop/CDNNRIA/Paper48/Authors
comment: We thank you for your detailed comments. However, we defend the novelty of our work.

As mentioned in our paper, RC is not a new idea and it is well known that RNN shares the weights. But to our knowledge, no works explicitly view RC as a model compression strategy and no works compare RC networks with their corresponding standard ones (with exactly the same computational graph) strictly. Thus, we believe that both "viewing RCNN a potential model compression strategy" and "using independent Batch normalization to increase accuracy" are our main contributions. The experimental results in our paper show that there is heavy redundancy across layers. This may inspire future works.

We have updated our paper. We compare the convergence speed in appendix A. The convergence speed of RC networks and the standard ones is similar. In another word, RC networks are easy to train. You can regard the depth of each network as an approximate measurement of its number of operations.

We choose ResNet because it is well known. We will test our approach on other network architectures.  To be honest, the network architecture is not an important factor in our opinion.

The use of the invertible downsampling operation is optional. One can replace it with a convolutional layer plus an average pooling layer. But this will increase the number of parameters. For example, suppose the size of the feature map is 64 by 20 by 20 (C by H by W). After applying the invertible downsampling operation, its size becomes 256 by 10 by 10.

By the way, which paper "reference 1" means? It seems not the first reference of our paper.

From: NIPS.cc/2018/Workshop/CDNNRIA/Paper48/AnonReviewer1
title: Summary: In this paper, the author modified the recurrent convolution neural network (RCNN) with independent batch normalization. The experimental results on RCNN are compatible with the ResNet neural network architecture when it contains the same number of layer.
review: Key strengths: 
1- Independent Batch normalization on RCNN is a good idea since in the conventional neural network the weights of the convolutional layer are shared in order to extract the invariant and transient features. Sharing batch normalization does not have the key role in invarient and transient features.
2-    The method is straightforward and easy to understand
Key weaknesses:
1-    The paper should be revised as the writing is poor.
2-    The author claims that the main contribution of the paper is viewing RCNN a potential model compression strategy. However, in reference 1 RCNN is mentioned as a neural network architecture with fewer parameters and I believe the main contribution of this paper is using independent Batch normalization to increase accuracy. This contribution of paper should be changed.
3-    The number of operations and speed convergence ( # number of the epoch ) are not calculated  in the paper
4-    The approach is only tested on ResNet model 
5-    The approach was not Novel enough
Comment: 
1-	Why are invertible downsampling operation used in this paper?

rating: 3: Marginally above acceptance threshold
confidence: 3: The reviewer is absolutely certain that the evaluation is correct and very familiar with the relevant literature

From: NIPS.cc/2018/Workshop/CDNNRIA/Paper48/AnonReviewer7
title: The idea of recurrent convolutions and independent BN layers are interesting
review: Pros: 
1. The first intuition of this paper is that Recurrent Convolutions can share weights in the whole layer and thus reduce the redundancy across layers. The idea of recurrent convolutions for compression alone is very interesting. 
2. In addition, authors also find that directly unrolling each res-block several times during training is not satisfactory. Meanwhile, sharing/averaging BN layer parameters will lead to lower performance than without BN layers. I think this is also a good point but may need better justification:  authors state that "there are no reasons to expect the statistics of the input over different unrolling steps are the same" but more detailed proof statistics like Fig.1(b) will be much better.
3. Another justification of independent BN layers of authors is that independent BN layers improve the representation power, which also makes senses in my opinion.

Cons:
1.  Although the experiment comparison is fair, the authors should mention the reason for gradient clip since they are essential in training recurrent convolutional networks.
2. The converging speed/training curves of different networks are also complementary proofs (but not necessarily with 3 pages limit).
rating: 4: Top 50% of accepted papers, clear accept
confidence: 2: The reviewer is fairly confident that the evaluation is correct

From: NIPS.cc/2018/Workshop/CDNNRIA/Paper48/Authors
comment: Thanks to your positive evaluation of our work.
We are sorry for our poor writing. We will revise our paper as soon as possible. 
We will also add more experiment details in the appendix.

From: NIPS.cc/2018/Workshop/CDNNRIA/Paper48/Authors
comment: 
(1). Every model trained in this work uses the same gradient clip policy. So we believe the comparisons in table 1 and table  2 are fair. Our preliminary results show that gradient clip has nearly no effect on the standard models, but can improve the recurrent convolutional models. Without gradient clip, sometimes the loss will be "nan" when training the recurrent convolutional models.
(2). The convergence speed of Resnet-10 looks slightly faster than RC-4's. But there is no obvious difference. We will add more experiment details including the loss curves in the appendix as soon as possible.

From: NIPS.cc/2018/Workshop/CDNNRIA/Reviewers
comment: Thanks for the author who presents the interesting idea, recurrent convolution. But I have a few small questions:

1. Does the gradient clip technique alone improve the accuracy of every model?
2. The model capacity of RC-4 and ResNet-10 is clearly different, but RC-4's performance is better than ResNet-10. The paper doesn't compare the convergence speed of these two models, will the RC-4 converges slower? or how much slower?

Thanks a lot!

From: NIPS.cc/2018/Workshop/CDNNRIA/Paper48/AnonReviewer2
title: Given a network that consists of multiple convolutional layers, it is possible to achieve compression by tying together the filter weights of these successive convolutional layers. In doing so; however, one should be careful not to use independent batch normalization layers that go along with these convolutional layers.  The proposed approach is evaluated using ResNet-10 and ResNet-18 architectures on CIFAR-10 and CIFAR-100 datasets.  I feel that the paper suffers from poor writing.
review: 1. This paper makes two arguments about why it is beneficial to tie the filter weights of successive convolutional layers in (deep) NN containing many convolutional layers.  One of these arguments is that it can lead to network compression.  Results in Table 2 seems to support this claim.

2. The statements about the need of keeping batch normalization layers independent makes sense.  Results in Table 1 seems to validate our intuition.

3. My biggest issue with this paper is that it suffers from poor writing.  The very first sentence of the paper has a grammatical error.  Poor writing detracts from the contributions of this paper.  I strongly encourage that this paper be revised to correct any writing errors.

rating: 3: Marginally above acceptance threshold
confidence: 2: The reviewer is fairly confident that the evaluation is correct