A Case for Soft Loss Functions

Alexandra Uma; Tommaso Fornaciari; Dirk Hovy; Silviu Paun; Barbara Plank; Massimo Poesio

A Case for Soft Loss Functions

Alexandra Uma, Tommaso Fornaciari, Dirk Hovy, Silviu Paun, Barbara Plank, Massimo Poesio

Computer Science

Publikation: Konference artikel i Proceeding eller bog/rapport kapitel › Konferencebidrag i proceedings › Forskning › peer review

Abstract

Recently, Peterson et al. provided evidence of the benefits of using probabilistic soft labels generated from crowd annotations for training a computer vision model, showing that us- ing such labels maximizes performance of the models over unseen data. In this paper, we generalize these results by showing that training with soft labels is an effective method for using crowd annotations in several other AI tasks besides the one studied by Peterson et al., and also when their performance is compared with that of state-of-the-art methods for learning from crowdsourced data.

Originalsprog	Engelsk
Titel	Proceedings of the eighth AAAI Conference on Human Computation and Crowdsourcing
Forlag	AAAI Press
Publikationsdato	2020
Status	Udgivet - 2020

Emneord

Probabilistic soft labels
Crowd annotations
Computer vision model
AI tasks
Crowdsourced data

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HCOMP_2020_paper_51Accepteret manuskript, 152 KB

Citationsformater

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AU - Uma, Alexandra

AU - Fornaciari, Tommaso

AU - Hovy, Dirk

AU - Paun, Silviu

AU - Plank, Barbara

AU - Poesio, Massimo

PY - 2020

Y1 - 2020

N2 - Recently, Peterson et al. provided evidence of the benefits of using probabilistic soft labels generated from crowd annotations for training a computer vision model, showing that us- ing such labels maximizes performance of the models over unseen data. In this paper, we generalize these results by showing that training with soft labels is an effective method for using crowd annotations in several other AI tasks besides the one studied by Peterson et al., and also when their performance is compared with that of state-of-the-art methods for learning from crowdsourced data.

AB - Recently, Peterson et al. provided evidence of the benefits of using probabilistic soft labels generated from crowd annotations for training a computer vision model, showing that us- ing such labels maximizes performance of the models over unseen data. In this paper, we generalize these results by showing that training with soft labels is an effective method for using crowd annotations in several other AI tasks besides the one studied by Peterson et al., and also when their performance is compared with that of state-of-the-art methods for learning from crowdsourced data.

KW - Probabilistic soft labels

KW - Crowd annotations

KW - Computer vision model

KW - AI tasks

KW - Crowdsourced data

KW - Probabilistic soft labels

KW - Crowd annotations

KW - Computer vision model

KW - AI tasks

KW - Crowdsourced data

M3 - Article in proceedings

BT - Proceedings of the eighth AAAI Conference on Human Computation and Crowdsourcing

PB - AAAI Press

ER -

A Case for Soft Loss Functions

Abstract

Emneord

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