Wasserstein Barycenter Model EnsemblingDownload PDF

Published: 21 Dec 2018, Last Modified: 21 Apr 2024ICLR 2019 Conference Blind SubmissionReaders: Everyone
Abstract: 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.
Keywords: Wasserstein barycenter model ensembling
TL;DR: we propose to use Wasserstein barycenters for semantic model ensembling
Data: [COCO](https://paperswithcode.com/dataset/coco), [ImageNet](https://paperswithcode.com/dataset/imagenet), [Places](https://paperswithcode.com/dataset/places)
Community Implementations: [![CatalyzeX](/images/catalyzex_icon.svg) 1 code implementation](https://www.catalyzex.com/paper/arxiv:1902.04999/code)
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