Quantification in-the-wild: data-sets and baselines
This work addresses quantification challenges for marine ecology researchers, but it is incremental as it applies known methods to new datasets without introducing novel techniques.
The paper tackled the problem of estimating class distributions in real-world datasets, specifically in marine ecology, by applying existing quantification methods to large-scale coral reef and plankton datasets and demonstrating that a fine-tuned deep neural network with minimal data (25-100 samples) outperforms alternatives.
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.