Pixel space augmentation has grown in popularity in many Deep Learning areas, due to its effectiveness, simplicity, and low computational cost.
Depth models trained with SUB-Depth outperform the same models trained in a standard single-task SDE framework.
Consistency regularization describes a class of approaches that have yielded state-of-the-art results for semi-supervised classification.
Human enterprise often suffers from direct negative effects caused by jellyfish blooms.
Virtual Adversarial Training has recently seen a lot of success in semi-supervised learning, as well as unsupervised Domain Adaptation.
Consistency regularization describes a class of approaches that have yielded ground breaking results in semi-supervised classification problems.
In this paper we test the use of a deep learning approach to automatically count Wandering Albatrosses in Very High Resolution (VHR) satellite imagery.
We analyze the problem of semantic segmentation and find that its' distribution does not exhibit low density regions separating classes and offer this as an explanation for why semi-supervised segmentation is a challenging problem, with only a few reports of success.