Reviews: Incremental Few-Shot Learning with Attention Attractor Networks

The authors proposed a new attention attractor for incremental few-shot learning where base classifier is trained offline with enough number of data and additional extra novel classes are added later, each with only a few labeled examples. The setting is important and interesting. The idea is novel and results are overall quite strong. There are some concerns regarding the clarity; this should be revised in the final version.