We study the problem of training machine learning models incrementally with batches of samples annotated with noisy oracles. We select each batch of samples that are important and also diverse via clustering and importance sampling. In particular, we incorporate model uncertainty into the sampling probability to compensate poor estimation of the importance scores when the training data is too small to build a meaningful model. Experiments on benchmark image classification datasets (MNIST, SVHN, and CIFAR10) shows improvement over existing active learning strategies. We introduce an extra denoising layer to deep networks to make active learning robust to label noises and show significant improvements.
Summary: The paper proposes an uncertainty-based method for batch-mode active learning with/without noisy oracles which uses importance sampling scores of clusters as the querying strategy. Authors evaluate their method on MNIST, CIFAR10, and SVHN against approaches such as Core-set, BALD, entropy, and random sampling and show superior performance.
Pros:
(+): The paper is well-written and well-motivated.
(+): The problem is timely and has direct real world applications.
(+): Applying the denoising layer is an interesting and viable idea to overcome noise effects.
 
Cons that significantly affected my score and resulted in rejecting the paper are as follows:
 
1 - Experimental setting and evaluations:
The biggest drawback in this paper is the experimental setting which is not rigorous enough for the following reasons:
(a) Weak datasets: Authors have chosen some standard benchmarks but they do not seem to be convincing as the datasets are too easy. Based on my experience, the behavior of an active learning agent trained on small number of classes does not necessarily generalize to cases where the number of classes is large. So I’d like to ask authors to try to evaluate on datasets with more number of classes as well as more realistic images (as opposed to thumbnail images). 
(b) Comparison to state of the art: More importantly, authors are missing out on an important baseline which is a recent ICCV paper [1] on task-agnostic pool-based batch-mode active learning that has explored both noisy and perfect oracles and to the best of my knowledge is the current state of the art. Authors can extend their experimental setting to the datasets used in [1] including CIFAR100 and ImageNet and provide comparison. The reason that it is important to compare is that the method in [1] is task-agnostic and does not explicitly use uncertainty hence it is interesting to see how this method performs against it. 
(c) More on baselines and related work: In addition to [1], different variation of ensemble methods have been serving as active baselines in this field and I recommend adding one as a baseline. For a recent work in this line you can see this paper from CVPR 2018 [2]. Moreover, the authors seem to be missing on a long-standing line of active learning research known as Query-by-Committee (QBC) began in 1997 [3] in the related work section which should be cited as well. 
(d) Hyper parameter tuning: Last but not least about the experiments is the hyper parameter tuning which is not addressed. It is important to not use the well-known hyper parameters for these benchmarks that have been obtained using validation set from the entire dataset. Authors should explain how they have performed this. 
 
2 - Report sampling time:
Another important factor missing in the evaluations is reporting time complexity or wall-clock time that it takes to query samples. Authors should measure this precisely and make sure it is being reported similarly across all the methods. I am asking this because random selection is still an effective baseline in the field and it only takes a few milliseconds. Therefore, the sampling time of a new algorithm should be gauged based on that while performing better than random. Given the multiple steps in this algorithm I am skeptical that the sampling time would be proportional to the gain obtained in accuracy versus labeling ratio over random selection baseline. 
 
3: Section 5.2 is not informative:
(a) My last major concern is section 5.2 where the discussion on results is given along with supporting figures. 
Lack of quantitative results: First of all, no quantitative results are given for the values plotted in figure 3 and 4 (neither in the main text nor in the supplement) and different methods happen to be too close to each other, making it hard to see the right color for standard deviations. Also, in the discussion corresponding to those figures no information is provided in this regard. It is important to report how much labeling effort this algorithm is saving by comparing number of samples needed by each method to achieve the same accuracy because that is the main goal in AL. Lack of numbers also makes it hard for this work to be used by others.
(b) Figure legends: The way authors have labeled their method in Figure 3 is confusing as the “Proposed+noise” happens to achieve better performance over “Proposed”. I think by “noise” authors meant denoising layer was being used (please correct me if I am wrong) but this is not what the legends imply. 
(c) X axis label: It is common to report accuracy versus percentage of labeled data making it more understandable of how far each experiment has been through each dataset. Additionally, I recommend reporting the maximum achievable accuracy for each dataset assuming that all the data was labeled. This serves as an upper bound.
(d) Font sizes in figures: It will be helpful to make them larger.
  
4. I also have a more general concern about uncertainty-based methods. I know that they have been around for a long time but given the fact that predictive uncertainty is still an open problem and there is still no concrete method to measure calibrated confidence scores for outputs of a deep network (Dropout and BN given in this paper have been already outperformed by ensembles (see [4])), hence relying on uncertainty is not the best direction to go. It is literally chicken and egg problem to try to rely on confidence scores of the main-stream task while it is being trained itself. This issue has been raised in this paper but I am still not convinced that the paper has fully addressed it. I think the community needs to explore task-agnostic methods more deeply. [1] is a good start on this path but there is always more to do. This concern is not necessarily a major part of my decision assessment and I only want the authors to state their opinion on this and explain how accurately they think this issue is being addressed.
 
The following issues are less major and are given only to help, and not part of my decision assessment:
1- In Figure 3(c), it appears that the accuracy for “Proposed + noise” when \epsilon=0.1 is higher than when it is noise-free. It might be a miss-reading as the figure is coarse and it is hard to compare but if that is the case, can authors explain it?
2- The Abstract does not read well and does not state the main contribution. It has put too emphasize on batch-mode active learning which has become an intuitive approach since deep networks have become popular. Also the wording “Our approach bridges between uniform randomness and score based importance sampling of clusters” should be changed as all other active learning algorithms are trying to do that. 
3 - In section 5.1 please state that you used VGG 16 (I assume so since it is what was used in the cited reference (Gal et al. 2017) but authors need to verify that. Also, the other citation given for this (Fchollet, 2015) is confusing as it is Keras package documentation while in the next sentence authors state that they have implemented their algorithm in PyTorch. So please shed some light on this.
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As a final note, I would be willing to raise my score if authors make the experimental setting stronger (see suggestions above).
[1] Sinha, Samarth, Sayna Ebrahimi, and Trevor Darrell. "Variational Adversarial Active Learning." arXiv preprint arXiv:1904.00370 (2019). 
[2] Beluch, William H., et al. "The power of ensembles for active learning in image classification." Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2018.
[3] Freund, Yoav, et al. "Selective sampling using the query by committee algorithm." Machine learning 28.2-3 (1997): 133-168.
[4] Lakshminarayanan, Balaji, Alexander Pritzel, and Charles Blundell. "Simple and scalable predictive uncertainty estimation using deep ensembles." Advances in Neural Information Processing Systems. 2017.
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POST-REBUTTAL:
In the revised version, there are new tables (Table 1-4) provided in the appendix which I found too different than results reported for previous baselines by more than 6%. For example, according to Core-set paper (Sener, 2018), Figure 4, they achieve near 80% using 40% of the data (20000 samples), and according to VAAL paper (Sinha et al. 2019 github page: https://github.com/sinhasam/vaal/blob/master/plots/plots.ipynb), they achieve 80.90+-0.2. However, the current paper reports 71.99 ± 0.55 for Core-set, and 74.06 ± 0.47 for VAAL which is a large mismatch.
More importantly, looking at the results provided in VAAL paper (Sinha et al. 2019 or Core-set paper (Sener, 2018) they show their performance as well as most of their baselines is superior to random selection by a large gap, but in this paper results shown in Table 1 to 4 in almost all of them random is superior (or on-par) to all baselines and the proposed method is the only method that outperforms baseline which is clearly a wrong claim. Therefore, I decrease my score from weak reject to reject.