UNSUPERVISED LEARNING VIA META-LEARNING
A central goal of unsupervised learning is to acquire representations from unlabeled data or experience that can be used for more effective learning of downstream tasks from modest amounts of labeled data. Many prior unsupervised learning works aim to do so by developing proxy objectives based on reconstruction, disentanglement, prediction, and other metrics. Instead, we develop an unsupervised meta-learning method that explicitly optimizes for the ability to learn a variety of tasks from small amounts of data. To do so, we construct tasks from unlabeled data in an automatic way and run meta-learning over the constructed tasks. Surprisingly, we find that, when integrated with meta-learning, relatively simple task construction mechanisms, such as clustering embeddings, lead to good performance on a variety of downstream, human-specified tasks. Our experiments across four image datasets indicate that our unsupervised meta-learning approach acquires a learning algorithm without any labeled data that is applicable to a wide range of downstream classification tasks, improving upon the embedding learned by four prior unsupervised learning methods.
In this paper, the task of performing meta-learning based on the unsupervised dataset is considered. The high-level idea is to generate 'pseudo-labels' via clustering of the given dataset using existing unsupervised learning techniques. Then the meta-learning algorithm is trained to easily discriminate between such labels. This paper seems to be tackling an important problem that has not been addressed yet to my knowledge. While the proposed method/contribution is quite simple, it possesses great potential for future applications and deeper exploration. The empirical results look strong and tried to address important aspects of the algorithm. The writing was clear and easy to follow. I especially liked how the authors tried to exploit possible pitfalls of their experimental design. 
Minor comments and questions:
- Although the problem of interest is non-trivial and important, the proposed algorithm can be seen as just a naive combination of clustering and meta-learning. It would have been great to see some clustering algorithm that was specifically designed for this type of problem. Especially, the proposed CACTUs algorithm relies on sampling without replacement from the clustered dataset in order to enforce "balance" of the labels among the generated task. This might be leading to suboptimal results since the popularity of each cluster (i.e., how much it represents the whole dataset) is not considered. 
- CACTUs seems to be relying on having random scaling of the k-means algorithm in order to induce diversity on the set of partitions being generated. I am a bit skeptical about the effectiveness of such a method for diversity. If this holds, it would be interesting to see the visualization of such a concept.
- Although only MAML was considered as the meta-learning algorithm, it would have been nice to consider one or more candidates to show that the proposed framework is generalizable. Still, I think the experiment is persuasive enough to expect that the algorithm would work well at practice.
- Would there be a trivial generalization of the algorithm to semi-supervised learning?  
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I am satisfied with the author's response and changes they made to the text. I still think the paper brings significant contributions to the area, by showing that even generating the pseudo-tasks via unsupervised clustering method allows the meta-learning to happen.