Embedding Adaptation is Still Needed for Few-Shot Learning

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Constructing new and more challenging tasksets is a fruitful methodology to analyse and understand few-shot classification methods. Unfortunately, existing approaches to building those tasksets are somewhat unsatisfactory: they either assume train and test task distributions to be identical --- which leads to overly optimistic evaluations --- or take a "worst-case" philosophy --- which typically requires additional human labor such as obtaining semantic class relationships. We propose ATG, a principled clustering method to defining train and test tasksets without additional human knowledge. models train and test task distributions while requiring them to share a predefined amount of information. We empirically demonstrate the effectiveness of in generating tasksets that are easier, in-between, or harder than existing benchmarks, including those that rely on semantic information. Finally, we leverage our generated tasksets to shed a new light on few-shot classification: gradient-based methods --- previously believed to underperform --- can outperform metric-based ones when transfer is most challenging.


More details to be added. Webpage under construction.

Séb Arnold - seb.arnold@usc.edu


Adapting your feature extractor is required when transfer from train to test tasks is most challenging.


In submission