Paper:
Few-Shot Adversarial Domain Adaptation
This work provides a framework for addressing the problem of supervised domain adaptation with deep models. The main idea is to exploit adversarial learning to learn an embedded subspace that simultaneously maximizes the confusion between two domains while semantically aligning their embedding. The supervised setting becomes attractive especially when there are only a few target data samples that need to be labeled. In this few-shot learning scenario, alignment and separation of semantic probability distributions is difficult because of the lack of data. We found that by carefully designing a training scheme whereby the typical binary adversarial discriminator is augmented to distinguish between four different classes, it is possible to effectively address the supervised adaptation problem. In addition, the approach has a high "speed" of adaptation, i.e. it requires an extremely low number of labeled target training samples, even one per category can be effective. We then extensively compare this approach to the state of the art in domain adaptation in two experiments: one using datasets for handwritten digit recognition, and one using datasets for visual object recognition.

Review:
Overall
The paper tackles the problem of domain adaptation when a few (up to 4) unlabeled samples exist in the target domain. An approach is presented based on the idea of adversarial training. The main contribution of the paper is in proposing an augmentation approach to alleviate the scarcity of target domain data, that is by taking pairs of source-target samples as input to combinatorially increase the number of available training samples. The approach proves to be very effective on two standard domain adaptation benchmarks, achieving new state-of-the-art results using only 4 labelled samples. 
The problem is very relevant in general and the achieved results are substantial. This by itself warrants the paper to be published in NIPS. However, several important experiments are missing for a better and in-depth understanding of the proposed method. Should it have included those experiments, I’d have rated it as top 15% papers.
Related Works
+ A structured and thorough coverage of the related works regarding domain adaptation.
- related works are missing which have GAN’s discriminator to also predict class labels as in this work
- related works are missing on using pairs of samples as for augmenting low number of available data, especially for learning common embedding space
Approach
+ The incremental novelty of the augmentation technique along with the new loss for the context of deep adversarial domain adaptation is a plausible and effective approach.
- Many details are missing: batchsize, learning rate, number of general iterations, number of iterations to train DCD and g_t/h, number of trials in adversarial training of the embedding space before convergence, etc.
- introduction refers to the case where zero samples from a class is available, however this case is not discussed/studied neither in the approach section nor in the experiments.
Experiments
+ the results using only a few labelled samples are impressive. Specially, when compared with prior and concurrent works using similar approaches.
- Why not go above 4? It would be informative to see the diminishing return
- A systematic study of the components of the architecture is missing, specially comparing when g_t and g_s are shared and not, but also regarding many other architectural choices. 
- In sec 3.1 it is mentioned a classification loss is necessary for the better working of the adaptation model. It seems conceptually redundant, except with the added loss more weight can be put on classification as opposed to domain confusion. In that respect,  the experimental setup lacks demonstrating the importance of this factor in the loss function.
- It seems the most basic configuration of an adversarial training is used in this work, plots and figures showing the stability of convergence using the introduced losses and architectures is important.
- An experiment where for some classes no target sample is available?