Active learning typically focuses on training a model on few labeled examples alone, while unlabeled ones are only used for acquisition. In this work we depart from this setting by using both labeled and unlabeled data during model training across active learning cycles. We do so by using unsupervised feature learning at the beginning of the active learning pipeline and semi-supervised learning at every active learning cycle, on all available data. The former has not been investigated before in active learning, while the study of latter in the context of deep learning is scarce and recent findings are not conclusive with respect to its benefit. Our idea is orthogonal to acquisition strategies by using more data, much like ensemble methods use more models. By systematically evaluating on a number of popular acquisition strategies and datasets, we find that the use of unlabeled data during model training brings a spectacular accuracy improvement in image classification, compared to the differences between acquisition strategies. We thus explore smaller label budgets, even one label per class.
The authors study the problem of incorporating unsupervised (representation pre-training) learning and semi-supervised learning into active learning for image classification; specifically, performing pre-training before active learning starts [Caron, et al., 2018] and then applying inductive label propagation [Issen, et al., 2019] (slightly modification in the cost function to look more like importance sampling) before active learning querying occurs for each round (Algorithm 1).  The most novel technical innovation of this submission is the joint label propagation (jLP) querying function (which is a method of ‘spanning’ the learned manifold space).  Experiments are conducted on four (multi-class) image classification datasets (MNIST, SVHN, CIFAR-10, CIFAR-100), showing that unsupervised learning and semi-supervised learning can improve active learning on these datasets — although random selection often works better (as best as I can tell) implying that negative results are also a contribution of this paper. Finally, some active learning experiments are conducted using a per-round label budget of one example per class — also demonstrating mixed results with random sampling performing better in general. 
In my mind, this paper has two primary components: (1) taking the position that semi-supervised and unsupervised learning can improve overall performance and, in principle, help with active learning and (2) propose jLP, which is a learning algorithm agnostic approach to spanning the manifold space. However, jLP doesn’t really seem to work in general. Thus, the main result is the first point — updating previous (pre-deep learning) results on SS/US AL to deep learning. Honestly, I think the primary conclusion is that semi-supervised and unsupervised learning has improved over the past decade (especially semi-supervised learning for image classification). The second result is that active learning in deep learning (at least for this application) hasn’t kept up. Wrt to (1), as the authors have pointed out, many others have applied semi-supervised learning to AL (including more that the authors didn’t include). Additionally, many have used unsupervised learning for AL (which the authors seem less aware of) from pre-clustering (e.g., [Nguyen & Smeulders, Active Learning using Pre-clustering; ICML04]) to one/few-shot learning (e.g., [Woodward & Finn, Active One-Shot Learning; NeurIPS16 workshop]) to using pre-trained embeddings for many ‘real-world tasks’ (e.g., NER [Shen, et al., Deep Active Learning for Named Entity Recognition; ICLR18] using word2vec). Thus, the interesting question would be to compare multiple pre-training techniques and ideally the relative effect on the active learning component (assuming this is the focus of the paper). With respect to semi-supervised learning, they have validated that inductive label propagation [Issen, et al., 2019] works for this task, but haven’t shown that this helps with active learning. Since this is a negative results without a theoretical contribution, I would again expect trying several semi-supervised algorithm and evaluating their relative performance in general and wrt the active learning querying strategy. Accordingly, I don’t think the contribution of this work in its current state is sufficiently well-developed — and would lean toward rejecting in its current form.
Below are some additional detailed comments (some also covered above): 
— Given that this points toward a negative result, a more convincing direction to take would be to consider more combinations of unsupervised and semi-supervised approaches — specifically emphasizing how they affect the active learning component. This might point to more general findings and maybe toward a theory (maybe even consider a second application).
— The empirical emphasis is more around overall performance rather than the interaction between unsupervised representation learning and active learning, which is more toward the stated goal of the paper.
— Wouldn’t the right way to do (deep) representation learning in multiple rounds be to fine-tune at least some fraction of the time?  If the only claim is pre-training or pre-clustering, people certainly do this — just often not as a point of emphasis.
— The ‘first semi-supervised’ claim really only holds in the context of deep learning; however, scope is really more like semi-supervised applied to image classification, which would be a pretty narrow contribution in scope.
— Overall, there is a general overstatement of contributions and results: this is certainly not the first SSAL or USAL and the statement relative to deep learning is subtle; some of the empirical results are interesting, but I am not sure about ‘spectacular gains’ (and these gains aren’t seemingly due to the contribution of the paper).
— I don’t understand the ensemble model analogy in the abstract; is it because it is a ‘meta-algorithm’?
Some more positive notes: 
+ It is interesting that there is some contradictory evidence relative to [Wang, et al., 2017; Gal, et al., 2017]; this is probably worth digging into a bit deeper.
+ The experimental details well-described given space constraints.
In summary, there are some interesting observations that are probably worth pursuing. However, the current contribution is basically that: (1) active learning doesn’t seem to really help, (2) semi-supervised learning and unsupervised learning improve performance for this task. Since (1) was really the point of the paper (as stated) in the title, I don’t think there is enough here to accept in its current form.