1 code implementation • ICLR Workshop Learning_to_Learn 2021 • Mateusz Ochal, Massimiliano Patacchiola, Amos Storkey, Jose Vazquez, Sen Wang
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.
1 code implementation • 7 Jan 2021 • Mateusz Ochal, Massimiliano Patacchiola, Amos Storkey, Jose Vazquez, Sen Wang
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 evaluation.
no code implementations • 1 Jan 2021 • Mateusz Ochal, Massimiliano Patacchiola, Jose Vazquez, Amos Storkey, Sen Wang
Few-shot learning aims to train models on a limited number of labeled samples from a support set in order to generalize to unseen samples from a query set.
no code implementations • 16 Sep 2020 • Jean de Bodinat, Thomas Guerneve, Jose Vazquez, Marija Jegorova
Due to the expensive nature of field data gathering, the lack of training data often limits the performance of Automatic Target Recognition (ATR) systems.
no code implementations • 10 May 2020 • Mateusz Ochal, Jose Vazquez, Yvan Petillot, Sen Wang
Deep convolutional neural networks generally perform well in underwater object recognition tasks on both optical and sonar images.
no code implementations • 2 Mar 2020 • Marija Jegorova, Antti Ilari Karjalainen, Jose Vazquez, Timothy M. Hospedales
In this paper we present a novel simulation technique for generating high quality images of any predefined resolution.
no code implementations • 15 Oct 2019 • Marija Jegorova, Antti Ilari Karjalainen, Jose Vazquez, Timothy Hospedales
High-quality realistic sonar data simulation could be of benefit to multiple applications, including training of human operators for post-mission analysis, as well as tuning and validation of autonomous target recognition (ATR) systems for underwater vehicles.