Venue: ICLR 2020 Conference

Reviews:

final_score: rating: 6: Weak Accept
review_content: This paper examines combining two approaches of obtaining a trained spikingneural network (SNN). The first approach of previous work is converting the weights of a trained artificial neural network (ANN) with a given architecture, to the weights and thresholds of a SNN, and the second approach uses a surrogate gradient to train an SNN with backpropagation. The true novelty of the paper seems to be in showing that combining the two approaches sequentially, trains a SNN that requiresfewer timesteps to determine an output which achieves state of the art performance. This is summarized by Table 1. However, it does not mention how many epochs it takes to train an SNN from scratch, nor compare this to the total training time (ANN training + SNN fine-tuning) of their approach. They also claim a novel spike-time based surrogate gradient function (eq. 11), but it is very practicallysimilar to the ones explored in the referenced Wu. et al 2018 (eq. 27 for instance), and these should be properly contrasted showing that this novel surrogate function is actually helpful (the performance/energy efficiency might only come from the hybrid approach). The authors argue for SOTA performance in Table 2, but the comparison to other work doesn’t clearly separate their performance from the otherlisted works; For example the accuracy gain against Lee et al.,2019 only comes from the architecture being VGG16 as opposed to VGG9, as can be seen from comparing with the VGG9 architecture from Table 1, furthermore they take the sameamount of timesteps, which is supposed to be the principle gain of this work.

Some small suggestions that are independent from the above:

1.The most similar or relevant version of equation (2) in previous work could be referenced nearby for context.

2.The last sentence of the first paragraph on p.4 “the outputs from each copy...” is confusing. Are you just meaning to describe BPTT? 

3.Typos: sec7 4th line “neruons”, sec 2.2 “both the credit” (remove “the”)

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Following the author response I have upgraded my rating.
review_title: Official Blind Review #4

final_score: rating: 6: Weak Accept
review_content: This paper proposes methods to initialize and train spiking NNs (SNNs) as an alternative to ANNs, not driven primarily by improved loss or generalization, but by energy efficiency improvements derived from timing-event based sparse operation rather than asynchronous sweeps. The backpropagation method introduced is sensible, as are the experiments on known datasets to show its effectiveness. The paper is well written (apart from the miniscule Figure 3 containing the main result). 
I recommend acceptance, with caveats: the energy performance is actually not directly calculated, but speculatively estimated, it depends on the computational architecture chosen to implement the respective networks. I point out that ANNs need to be trained first to properly initialize an SNN, so the relative training effort claimed is less impressive, but energy performance does count in actual operational practice - training is (or should) be a small fraction of that.
review_title: Official Blind Review #3

final_score: rating: 6: Weak Accept
review_content: This paper presents a fine-tuning method of models converted from standard encoding and SGD training to Spike/NN's. 
The key point of the paper is that directly training S/NN's with spike-back-prop is slow and inefficient, while directly inferencing with converted models is also inefficient due to the large integration window required to get a good estimate of the spiking neuron potential. The authors claim, and to a good extent show that, their proposed method is best of both worlds: train the models efficiently with standard encoding / SGD, this is something we know works and scale well, then convert and fine-tune with spike-backprop to get models that perform well under a shorter integration window, and thus are more efficient at inference time. The intuition is that models can achieve shorter integration windows while keeping good results because, under the assumptions made by the proposed algorithm, the fine-tuning is effectively unrolling neuron dynamics that can be trained with BPPT, in a way similar to LSTM/Recurrent models. In that case, since model dynamics are taken into account during fine-tuning, it results in better performance even under shorter time-windows. This is an interesting concept, since the training doesn't only consider a mean-field estimate of the spike-activation, but it looks at  spiking neuron dynamics with an higher granularity. The paper is well written, clear and easy to understand. Results are comparatively competitive and code is made available.
review_title: Official Blind Review #2

Meta-review: After the rebuttal, all reviewers rated this paper as a weak accept. 
The reviewer leaning towards rejection was satisfied with the author response and ended up raising their rating to a weak accept.  The AC recommends acceptance.
Decision: Accept (Poster)