Rank-based loss for learning hierarchical representations
This addresses hierarchical classification in audio, an underexplored area, with potential applications in domains using taxonomies, though it appears incremental as it builds on prior work like triplet loss.
The authors tackled the problem of learning hierarchical representations for audio classification by proposing a rank-based loss function that translates hierarchical information into a rank ordering of target distances, showing it can learn hierarchically correct representations for unseen fine-level classes.
Hierarchical taxonomies are common in many contexts, and they are a very natural structure humans use to organise information. In machine learning, the family of methods that use the 'extra' information is called hierarchical classification. However, applied to audio classification, this remains relatively unexplored. Here we focus on how to integrate the hierarchical information of a problem to learn embeddings representative of the hierarchical relationships. Previously, triplet loss has been proposed to address this problem, however it presents some issues like requiring the careful construction of the triplets, and being limited in the extent of hierarchical information it uses at each iteration. In this work we propose a rank based loss function that uses hierarchical information and translates this into a rank ordering of target distances between the examples. We show that rank based loss is suitable to learn hierarchical representations of the data. By testing on unseen fine level classes we show that this method is also capable of learning hierarchically correct representations of the new classes. Rank based loss has two promising aspects, it is generalisable to hierarchies with any number of levels, and is capable of dealing with data with incomplete hierarchical labels.