Search Results for author: Jose Vazquez

Found 7 papers, 2 papers with code

How Sensitive are Meta-Learners to Dataset Imbalance?

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.

Few-Shot Learning

Few-Shot Learning with Class Imbalance

1 code implementation7 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.

Few-Shot Learning

Class Imbalance in Few-Shot Learning

no code implementations1 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.

Few-Shot Learning

Similarity-based data mining for online domain adaptation of a sonar ATR system

no code implementations16 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.

Online Domain Adaptation

A Comparison of Few-Shot Learning Methods for Underwater Optical and Sonar Image Classification

no code implementations10 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.

Few-Shot Learning General Classification +3

Unlimited Resolution Image Generation with R2D2-GANs

no code implementations2 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.

Image Generation

Full-Scale Continuous Synthetic Sonar Data Generation with Markov Conditional Generative Adversarial Networks

no code implementations15 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.

Cannot find the paper you are looking for? You can Submit a new open access paper.