Hierarchical document categorisation is a special case of multi-label document categorisation, where there is a taxonomic hierarchy among the labels.
Besides, the adversarial loss aggressively encourages the output image to be close to the distribution of the ground truth.
In computer vision, superpixels have been widely used as an effective way to reduce the number of image primitives for subsequent processing.
The effectiveness of gene expression pattern annotation relies on the quality of feature representation.
In this paper we propose a two-stage domain adaptation methodology which combines weighted data from multiple sources based on marginal probability differences (first stage) as well as conditional probability differences (second stage), with the target domain data.