Wasserstein Barycenter Model Ensembling

Pierre Dognin, Igor Melnyk, Youssef Mroueh, Jerret Ross, Cicero Dos Santos, Tom Sercu

In this paper we propose to perform model ensembling in a multiclass or a multilabel learning setting using Wasserstein (W.) barycenters. Optimal transport metrics, such as the Wasserstein distance, allow incorporating semantic side information such as word embeddings. Using W. barycenters to find the consensus between models allows us to balance confidence and semantics in finding the agreement between the models. We show applications of Wasserstein ensembling in attribute-based classification, multilabel learning and image captioning generation. These results show that the W. ensembling is a viable alternative to the basic geometric or arithmetic mean ensembling.

Knowledge Graph

arrow_drop_up

Comments

Sign up or login to leave a comment