Weakness:
1: The paper focus over the metric learning approach of meta-learning, what about the optimization-based model e.g. MAML [a], Implicit-MAML [b] etc?. How the model will behave over these approaches? Does the same analysis be correct for other meta-learning model?
2: The base model is implemented based on ProtoNet (Deleu et al., 2019) with default parameters given by (Medina et al., 2020). How we can ensure there hyperparameters are optimal?
3: For the domain invariant auxiliary learning, “treat each image as its own class to form the support” how it is a valid labelling? It may be a wrong class assignment, then the result may be worse than using this, but the model works. I cannot understand the proper intuition, could you please provide the detail intuition?
4: Is it possible to add some optimization based meta-learning approach in the Table-1? like MAML/implicit-MAML?
5: What is the size of auxiliary data?
[a] Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks
[b] Meta-Learning with Implicit Gradients
4: Is it possible to add some optimization based meta-learning approach in the Table-1? like MAML/implicit-MAML?