LEARNING TO LEARN WITH CONDITIONAL CLASS DEPENDENCIES
Neural networks can learn to extract statistical properties from data, but they seldom make use of structured information from the label space to help representation learning. For example "cat" and "dog" are closer than "cat" and "truck". Although some label structure can implicitly be obtained when training on huge amounts of data, in a few-shot learning context where little data is available, making explicit use of the label structure can inform the model to reshape the representation space to reflect a global sense of class dependencies. We propose a meta-learning framework, Conditional class-Aware Meta-Learning (CAML), that conditionally transforms feature representations based on a metric space that is trained to capture inter-class dependencies. This enables a conditional modulation of the feature representations of the base-learner to impose regularities informed by the label space. Experiments show that the conditional transformation in CAML leads to more disentangled representations and achieves competitive results on the miniImageNet benchmark.
[Summary]
The paper presents an enhancement to the Model-Agnostic Meta-Learning (MAML) framework to integrate class dependency into the gradient-based meta-learning procedure. Specifically, the class dependency is encoded by embedding the training examples via a clustering network into a metric space where semantic similarity is preserved via affinity under Euclidean distance. Embedding of an example in this space is further employed to modulate (scale and shift) features of the example extracted by the base-learner via a transformation network, and the final prediction is made on top of the modulated features. Experiments on min-ImageNet shows that the proposed approach improves the baseline of MAML.    
Pros
- An interesting idea of leveraging class dependency in meta-learning.
- Solid implementation with reasonable technical solutions.
Cons
- Some relevant interesting areas/cases were not exploited/tested.
- Improvement over state-of-the-arts (SOA) is marginal or none. 
[Originality]
The paper is motivated by an interesting observation that class dependency in the label space can also provide insights for meta-learning. This seems to be first introduced in the context of  meta-learning.
[Quality]
Overall the paper is well executed in some aspects, including motivation and technical implementation. There are, however, a few areas/cases I would like to see more from it so as to make a stronger case. 
In terms of generalization, the proposed enhancement to MAML is claimed to be orthogonal to other SOAs that are also within the framework based on gradient-descent, e.g. LEO. It is not quite clear to me that if the use of class dependency can lead to general benefits to alike methods like LEO, or if it is just a specific case for the MAML baseline. Actually, it would be interesting to see how the proposed class-conditional modulation can help other SOA in table 1. Also, more empirical results from other use cases (e.g., other datasets or problems) also help provide more insights here. These augmentation can better justify the value or significance of this work.       
In the specific formulation of the approach in Fig 2, it looks to me that the whole system is a compounded framework that combines two classifiers with one (base-learner) producing base representation, and the second injects side-information (e.g., from class-dependency in this case) to modulates the base representation before the final prediction. I just wonder what would happen if similar process keeps on? E.g., by building the third stage that modulates the features from the previous two? Or what if we swap the roles of base-learner and the embedding from the metric space (i.e., using the base-learner to modulate the embedding)? It looks to me that the feature/embedding from both components (in Fig 5 and 6) are optimized to improve separability. The roles they play in this process are also very interesting to get more elucidation. 
 
Another point worth discussion is that the class dependency currently imposed does not see to include hierarchical structure among classes, i.e., the label space is still flat. It would be great if this can be briefly discussed with respect to the current formulation to better inspire the future work.
[Clarity]
The paper is generally well written and I did not have much difficulty to follow. 
[Significance]
While the paper is built on an interesting idea, there are still a few areas for further improvement to justify its significance (the the comments above).