Quantification in-the-wild: data-sets and baselines

Oscar Beijbom; Judy Hoffman; Evan Yao; Trevor Darrell; Alberto Rodriguez-Ramirez; Manuel Gonzalez-Rivero; Ove Hoegh - Guldberg

arXiv · 1510.04811

Quantification in-the-wild: data-sets and baselines

Oscar Beijbom·Judy Hoffman·Evan Yao·Trevor Darrell·Alberto Rodriguez-Ramirez·Manuel Gonzalez-Rivero·Ove Hoegh - Guldberg

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Abstract

Quantification is the task of estimating the class-distribution of a data-set. While typically considered as a parameter estimation problem with strict assumptions on the data-set shift, we consider quantification in-the-wild, on two large scale data-sets from marine ecology: a survey of Caribbean coral reefs, and a plankton time series from Martha's Vineyard Coastal Observatory. We investigate several quantification methods from the literature and indicate opportunities for future work. In particular, we show that a deep neural network can be fine-tuned on a very limited amount of data (25 - 100 samples) to outperform alternative methods.

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Oscar Beijbom, Judy Hoffman, Evan Yao, Trevor Darrell, Alberto Rodriguez-Ramirez, Manuel Gonzalez-Rivero, Ove Hoegh - Guldberg. 2015-11-28. Quantification in-the-wild: data-sets and baselines. https://arxiv.org/abs/1510.04811

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