Quantification in-the-wild: data-sets and baselines
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.
Keywords
Cite
@article{arxiv.1510.04811,
title = {Quantification in-the-wild: data-sets and baselines},
author = {Oscar Beijbom and Judy Hoffman and Evan Yao and Trevor Darrell and Alberto Rodriguez-Ramirez and Manuel Gonzalez-Rivero and Ove Hoegh - Guldberg},
journal= {arXiv preprint arXiv:1510.04811},
year = {2015}
}
Comments
This report was prsented at the NIPS 2015 workshop on Transfer and Multi-Task Learning: Trends and New Perspectives. It is 4 pages + 1 page of references followed by a 6 page appendix