198 citations · 200 across the 4 of their papers we have counts for
10 papers
Domain Randomization for Object Counting
Enric Moreu, Kevin McGuinness, Diego Ortego +1
Recently, the use of synthetic datasets based on game engines has been shown to improve the performance of several tasks in computer vision. However, these datasets are typically o…
How Important is Importance Sampling for Deep Budgeted Training?
Eric Arazo, Diego Ortego, Paul Albert +2
Long iterative training processes for Deep Neural Networks (DNNs) are commonly required to achieve state-of-the-art performance in many computer vision tasks. Importance sampling a…
Addressing out-of-distribution label noise in webly-labelled data
Paul Albert, Diego Ortego, Eric Arazo +2
A recurring focus of the deep learning community is towards reducing the labeling effort. Data gathering and annotation using a search engine is a simple alternative to generating…
Multi-Objective Interpolation Training for Robustness to Label Noise
Diego Ortego, Eric Arazo, Paul Albert +2
Deep neural networks trained with standard cross-entropy loss memorize noisy labels, which degrades their performance. Most research to mitigate this memorization proposes new robu…
Unsupervised Contrastive Learning of Sound Event Representations
Eduardo Fonseca, Diego Ortego, Kevin McGuinness +2
Self-supervised representation learning can mitigate the limitations in recognition tasks with few manually labeled data but abundant unlabeled data---a common scenario in sound ev…
Reliable Label Bootstrapping for Semi-Supervised Learning
Paul Albert, Diego Ortego, Eric Arazo +2
Reducing the amount of labels required to train convolutional neural networks without performance degradation is key to effectively reduce human annotation efforts. We propose Reli…