4 citations · 4 across the 5 of their papers we have counts for
3 papers · 1 filter
How Sensitive are Meta-Learners to Dataset Imbalance?
Mateusz Ochal, Massimiliano Patacchiola, Amos Storkey +2
Meta-Learning (ML) has proven to be a useful tool for training Few-Shot Learning (FSL) algorithms by exposure to batches of tasks sampled from a meta-dataset. However, the standard…
Few-Shot Learning with Class Imbalance
Mateusz Ochal, Massimiliano Patacchiola, Amos Storkey +2
Few-Shot Learning (FSL) algorithms are commonly trained through Meta-Learning (ML), which exposes models to batches of tasks sampled from a meta-dataset to mimic tasks seen during…
Full-Scale Continuous Synthetic Sonar Data Generation with Markov Conditional Generative Adversarial Networks
Marija Jegorova, Antti Ilari Karjalainen, Jose Vazquez +1
Deployment and operation of autonomous underwater vehicles is expensive and time-consuming. High-quality realistic sonar data simulation could be of benefit to multiple application…