  Few-shot learning aims to generalize unseen classes that appear during
testing but are unavailable during training. Prototypical networks incorporate
few-shot metric learning, by constructing a class prototype in the form of a
mean vector of the embedded support points within a class. The performance of
prototypical networks in extreme few-shot scenarios (like one-shot) degrades
drastically, mainly due to the desuetude of variations within the clusters
while constructing prototypes. In this paper, we propose to replace the typical
prototypical loss function with an Episodic Triplet Mining (ETM) technique. The
conventional triplet selection leads to overfitting, because of all possible
combinations being used during training. We incorporate episodic training for
mining the semi hard positive and the semi hard negative triplets to overcome
the overfitting. We also propose an adaptation to make use of unlabeled
training samples for better modeling. Experimenting on two different audio
processing tasks, namely speaker recognition and audio event detection; show
improved performances and hence the efficacy of ETM over the prototypical loss
function and other meta-learning frameworks. Further, we show improved
performances when unlabeled training samples are used.
