Although few-shot learning and one-class classification (OCC), i.e. learning a binary classifier with data from only one class, have been separately well studied, their intersection remains rather unexplored. Our work addresses the few-shot OCC problem and presents a meta-learning approach that requires only few data examples from only one class to adapt to unseen tasks. The proposed method builds upon the model-agnostic meta-learning (MAML) algorithm ) and learns a model initialization particularly suited for learning few-shot OCC tasks. This is done by explicitly optimizing for a parameter initialization which only requires a few gradient steps with one-class minibatches to yield a performance increase on class-balanced test data. We provide a theoretical analysis that explains why our approach works in the few-shot OCC scenario, while other meta-learning algorithms, including MAML, fail. Empirical results on six datasets from the image and time-series domains show that our method substantially outperforms both, classical OCC and few-shot classification approaches, and demonstrate the ability to quickly learn unseen tasks from only few normal class samples. Moreover, we successfully learn anomaly detectors for a real-world application on sensor readings recorded during industrial manufacturing of workpieces with a CNC milling machine using a few examples from the normal class.
This paper tackles an interesting problem, one-class classification or anomaly detection, using a meta-learning approach. The main contribution is to introduce a parameter such that the inner-loop of the meta-learning algorithm better reflects the imbalance which occurs during meta-testing. Results are shown comparing a few simple baselines to both MAML and the modified variant, on a few datasets such as image-based ones (MNIST, miniImageNet), a synthetic dataset, and a real-world time-series example from CNC milling machines.
Overall, the paper presents an interesting problem and awareness that meta-learning might be general enough to solve it well, but provides no real novelty in the approach. The datasets and comparison to other state of art methods (including both other anomaly detection methods and out of distribution methods) is lacking. I suggest the authors perform more rigorous experimentation and focus the paper to be a paper about an understudied problem with rigorous experiments/findings, or improve their method beond the small modification made. Due to these weaknesses, I vote for rejection at this time. Detailed comments are below. 
Strengths
 
  - The problem is interesting and under-studied in the context of deep learning and transferable methods from similar ML problems (e.g. few-shot learning)
  - The method is simple and adapts a state of art in few-shot learning (meta-learning, and specifically MAML)
Weaknesses
  - While I enjoyed reading the paper since it tackles an under-explored problem, it is hard to justify publishing the method/approach at a top machine learning conference. Changing the balance in meta-learning is a relatively obvious modification that one would do to better reflect the problem; I don't think it results in general scientific/ML principles that can be used elsewhere. 
  - The relationship to out-of-distribution detection (which some of the experiments, e.g. Multi-task MNIST and miniImagenet essentially test) is not discussed or compared to. How are anomalies defined and is it really different than just being out-of-distribution? 
  - The datasets are limited. The MNIST dataset seems to choose a fixed two specific categories for meta-validation and meta-testing, as opposed to doing cross-validation. Results on just one meta-testing seems limited in this case with just one class. In terms of time-series, anomaly detection has been studied for a long time; is there a reason that the authors create a new synthetic dataset? For the milling example, how were anomalies provoked?
  - The baselines do not represent any state of art anomaly detection (e.g. density based, isolation forests, etc.) nor out of distribution detection; the latter especially would likely do extremely well for the simple image examples. 
  - There is no analysis of what the difference is in representation (initialization) learning due to the differences between the OCC and FS setup. What are the characteristics of the improved initialization?
One minor comment not reflecting the decision:
  - Exposition: Define the one-class classification problem; it's not common so it would be good to define in the abstract, or mention anomaly detection which is a better-known term.