Snapshot Ensembles: Train 1, Get M for Free
Ensembles of neural networks are known to be much more robust and accurate than individual networks. However, training multiple deep networks for model averaging is computationally expensive. In this paper, we propose a method to obtain the seemingly contradictory goal of ensembling multiple neural networks at no additional training cost. We achieve this goal by training a single neural network, converging to several local minima along its optimization path and saving the model parameters. To obtain repeated rapid convergence, we leverage recent work on cyclic learning rate schedules. The resulting technique, which we refer to as Snapshot Ensembling, is simple, yet surprisingly effective. We show in a series of experiments that our approach is compatible with diverse network architectures and learning tasks. It consistently yields lower error rates than state-of-the-art single models at no additional training cost, and compares favorably with traditional network ensembles. On CIFAR-10 and CIFAR-100 our DenseNet Snapshot Ensembles obtain error rates of 3.4% and 17.4% respectively.
I don*t have much to add to my pre-review questions. The main thing I*d like to see that would strengthen my review further is a larger scale evaluation, more discussion of the hyperparameters, etc. Where test error are reported for snapshot ensembles it would be useful to report statistics about the performance of individual ensemble members for comparison (mean and standard deviation, maybe best single member*s error rate).
You are a member of the scientific community tasked with peer review. Review the following paper title and abstract.
### Title + Abstract
Snapshot Ensembles: Train 1, Get M for Free
Ensembles of neural networks are known to be much more robust and accurate than individual networks. However, training multiple deep networks for model averaging is computationally expensive. In this paper, we propose a method to obtain the seemingly contradictory goal of ensembling multiple neural networks at no additional training cost. We achieve this goal by training a single neural network, converging to several local minima along its optimization path and saving the model parameters. To obtain repeated rapid convergence, we leverage recent work on cyclic learning rate schedules. The resulting technique, which we refer to as Snapshot Ensembling, is simple, yet surprisingly effective. We show in a series of experiments that our approach is compatible with diverse network architectures and learning tasks. It consistently yields lower error rates than state-of-the-art single models at no additional training cost, and compares favorably with traditional network ensembles. On CIFAR-10 and CIFAR-100 our DenseNet Snapshot Ensembles obtain error rates of 3.4% and 17.4% respectively.
user
<think>Okay, to do a decent peer review, I should start with summarizing what the work is about. This includes novelty, contributions, clarity, and impact. I might include suggestions to issues that I find critical to the usefulness of the work. Starting with novelty, there are different types of novelty. The first aspect of novelty is scope and scale. Is the work covering a broader perspective than previous works? The second aspect is methodology. Is the proposed methodology novel compared to previous methods? Do they provide any significant advantage over previous methodologies? The third aspect is results. Are the results different compared previous work? Does this lead to any novel theories or an extension of current theories? Next is clarity, I will be reading the work in depth and analyze if any sentences can be potentially ambiguous or misleading. If any sentences are ambiguous, I will state them and give a suggestion on how to rephrase such sentence or how to make more clear to the audience. Lastly, impact is important. I will attempt to predict how such work will impact the scientific community. This might include the applications of such work and how the work might transform the way we study a certain topic. Finally, I will might only mention issues that are critical. This is to keep the review as concise and helpful as possible.</think>



I don*t have much to add to my pre-review questions. The main thing I*d like to see that would strengthen my review further is a larger scale evaluation, more discussion of the hyperparameters, etc. Where test error are reported for snapshot ensembles it would be useful to report statistics about the performance of individual ensemble members for comparison (mean and standard deviation, maybe best single member*s error rate).
assistant
I don*t have much to add to my pre-review questions. The main thing I*d like to see that would strengthen my review further is a larger scale evaluation, more discussion of the hyperparameters, etc. Where test error are reported for snapshot ensembles it would be useful to report statistics about the performance of individual ensemble members for comparison (mean and standard deviation, maybe best single member*s error rate).