Deep Neural Networks (DNNs) often rely on very large datasets for training. Given the large size of such datasets, it is conceivable that they contain certain samples that either do not contribute or negatively impact the DNN's optimization. Modifying the training distribution in a way that excludes such samples could provide an effective solution to both improve performance and reduce training time. In this paper, we propose to scale up ensemble Active Learning methods to perform acquisition at a large scale (10k to 500k samples at a time). We do this with ensembles of hundreds of models, obtained at a minimal computational cost by reusing intermediate training checkpoints. This allows us to automatically and efficiently perform a training data subset search for large labeled datasets. We observe that our approach obtains favorable subsets of training data, which can be used to train more accurate DNNs than training with the entire dataset. We perform an extensive experimental study of this phenomenon on three image classification benchmarks (CIFAR-10, CIFAR-100 and ImageNet), analyzing the impact of initialization schemes, acquisition functions and ensemble configurations. We demonstrate that data subsets identified with a lightweight ResNet-18 ensemble remain effective when used to train deep models like ResNet-101 and DenseNet-121. Our results provide strong empirical evidence that optimizing the training data distribution can provide significant benefits on large scale vision tasks.
Review Summary
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Overall, I'm not quite convinced this method would be worth the trouble to implement. On the more realistic benchmarks, they need to keep ~80% of the total dataset size and the claimed "improvement" is rather small (less than 0.6% absolute gain in accuracy, e.g. from 81.86% to 82.37% on CIFAR100 and from 72.33% to 72.78% on ImageNet). There is no runtime comparison, there are missing baselines, and most of the method development seems guided by trying out many options instead of taking a principled approach. Without these, the paper is just not ready for a top conference like ICLR.
Paper Summary
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The paper considers a new take on active learning for image classification: given a large fully labeled dataset, identify a subset of the data that, when training on that subset alone, yields similar performance as training on the (much larger) full dataset. The paper focuses on "ensembles" of deep neural network classifiers as the prediction model, following Lakshminarayanan et al. (2017). 
The presented method is summarized in Algorithm 1. Given a suitably initialized "acquisition model", the model makes predictions on each example in the full dataset, then ranks examples using an acquisition function to find the subset of size N_s (top N_s examples by rank) where there is most "disagreement" among the model ensemble. This subset is then used to train a "subset" model (again, an ensemble of DNNs).
Experiments consider several possible initializations, acquisition functions, and ensemble sizes. Evaluation is done using the validation sets of three prominent image classification benchmarks: CIFAR10, CIFAR100, and ImageNet (1000 classes). 
Significance
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I don't think a successful case has been made that the proposed solution would generate significant widespread interest, because the gains demonstrated here are too minimal. Looking at the primary results in Table 2, it's really only when using 80% of the total images of imagenet or cifar100 (the most realistic benchmarks) that there is a small (<1%) absolute gain in accuracy over the simpler approach of just using the full dataset. Thus, the approach is not going to significantly reduce computational burden but adds a lot of complexity.
Novelty
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The method seems new to me.
Experimental Concerns
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## E1: Need to consider runtime in evaluation
None of the figures/tables that I can see report elapsed runtimes for the different methods. To me this is the fundamental tradeoff: not how many fewer examples can I learn from, but how much faster is the method than the "standard" of using the full dataset? Showing curves of validation progress over wallclock time would be a better way to present results. 
The important thing here is that even *full dataset* makes progress after each minibatch. You'd need to show progress at checkpoints for each epoch in  0.2, 0.4, 0.6, 0.8, 1, 1.2, 1.4, .... 
## E2: Potential missing baseline: Random subset with balanced classes
When you select a random subset, are you ensuring class balance? If not, that seems like a more refined baseline. Perhaps won't make too much difference for cifar10, but could be important for ensuring rarer classes in ImageNet are represented well. 
Presentation Concerns
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## P1: Initialization description confusing
I didn't understand the "build up" method as described in Sec. 3. How large is the subset used at each phase? How do you know when to stop? This could use a rewrite to improve clarity.
## P2: Missing some details for reproducibility
How was convergence assessed for all models? How were learning rates set? Many of these are crucial to understanding the runtime required for different models. (Sorry if these are in the appendix, but some short summary is needed in the main paper)
## P3: Title Change Recommended
I don't think the presented method is really doing a "Distribution Search"... I would suggest "Training Data Subset Selection with Ensemble Active Learning"
Minor Method Concerns
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## M1: What about regression?
Acquisition functions seem specialized to classification. What to do for regression or structure learning? Any general principles to recommend?