Workshop track -ICLR 2018 ANYTIME NEURAL NETWORK: A VERSATILE TRADE- OFF BETWEEN COMPUTATION AND ACCURACY
We present an approach for anytime predictions in deep neural networks (DNNs). For each test sample, an anytime predictor produces a coarse result quickly, and then continues to refine it until the test-time computational budget is depleted. Such predictors can address the growing computational problem of DNNs by automatically adjusting to varying test-time budgets. In this work, we study a general augmentation to feed-forward networks to form anytime neural networks (ANNs) via auxiliary predictions and losses. Specifically, we point out a blindspot in recent studies in such ANNs: the importance of high final accuracy. In fact, we show on multiple recognition data-sets and architectures that by having near-optimal final predictions in small anytime models, we can effectively double the speed of large ones to reach corresponding accuracy level. We achieve such speed-up with simple weighting of anytime losses that oscillate during training. We also assemble a sequence of exponentially deepening ANNs, to achieve both theoretically and practically near-optimal anytime results at any budget, at the cost of a constant fraction of additional consumed budget.
1. Paper Summary
This paper adds a separate network at every layer of a residual network that performs classification. They minimize the loss of every classifier using two proposed weighting schemes. They also ensemble this model.
2. High level paper
The organization of this paper is a bit confusing. Two weighing schemes are introduced in Section 3.1, then the ensemble model is described in Section 3.2, then the weighing schemes are justified in Section 4.1.
Overall this method is essentially an cascade where each cascade classifier is a residual block. Every input is passed through as many stages as possible until the budget is reached. While this model is likely quite useful in industrial settings, I don't think the model itself is wholly original.
The authors have done extensive experiments evaluating their method in different settings. I would have liked to see a comparison with at least one other anytime method. I think it is slightly unfair to say that you are comparing with Xie & Tu, 2015 and Huang et al., 2017 just because they use the CONSTANT weighing schemes.
3. High level technical
I have a few concerns:
- Why does AANN+LINEAR nearly match the accuracy of EANN+SIEVE near 3e9 FLOPS in Figure 4b but EANN+LINEAR does not in Figure 4a? Shouldn't EANN+LINEAR be strictly better than AANN+LINEAR?
- Why do the authors choose these specific weighing schemes? Section 4.1 is devoted to explaining this but it is still unclear to me. They talk about there being correlation between the predictors near the end of the model so they don't want to distribute weight near the final predictors but this general observation doesn't obviously lead to these weighing schemes, they still seem a bit adhoc.
A few other comments:
- Figure 3b seems to contain strictly less information than Figure 4a, I would remove Figure 3b and draw lines showing the speedup you get for one or two accuracy levels.
Questions:
- Section 3.1: "Such an ideal θ* does not exist in general and often does not exist in practice." Why is this the case? 
- Section 3.1: " In particular, spreading weights evenly as in (Lee et al., 2015) keeps all i away from their possible respective minimum" Why is this true?
- Section 3.1: "Since we will evaluate near depth b3L/4e, and it
is the center of L/2 low-weight layers, we increase it weight by 1/8." I am completely lost here, why do you do this?
4. Review summary
Ultimately because the model itself resembles previous cascade models, the selected weighings have little justification, and there isn't a comparison with another anytime method, I think this paper isn't yet ready for acceptance at ICLR.