Quantification in-the-wild: data-sets and baselines
arXiv:1510.04811
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.
arXiv commentThis 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
References in corpus (4)
- Caffe: Convolutional Architecture for Fast Feature Embedding
- Domain Adaptation for Statistical Classifiers
- WHOI-Plankton- A Large Scale Fine Grained Visual Recognition Benchmark Dataset for Plankton Classification
- Random Sampling in an Age of Automation: Minimizing Expenditures through Balanced Collection and Annotation