1 code implementation • 6 Jul 2023 • Mario Almagro, Emilio Almazán, Diego Ortego, David Jiménez
We show that this is also the case for sentence similarity, a fundamental task in multiple domains, e. g. matching, retrieval or paraphrasing.
no code implementations • 5 Jul 2022 • Mario Almagro, David Jiménez, Diego Ortego, Emilio Almazán, Eva Martínez
Product matching is a fundamental step for the global understanding of consumer behavior in e-commerce.
1 code implementation • 17 Feb 2022 • Enric Moreu, Kevin McGuinness, Diego Ortego, Noel E. O'Connor
We introduce a domain randomization approach for object counting based on synthetic datasets that are quick and inexpensive to generate.
1 code implementation • 27 Oct 2021 • Eric Arazo, Diego Ortego, Paul Albert, Noel E. O'Connor, Kevin McGuinness
We suggest that, given a specific budget, the best course of action is to disregard the importance and introduce adequate data augmentation; e. g. when reducing the budget to a 30% in CIFAR-10/100, RICAP data augmentation maintains accuracy, while importance sampling does not.
no code implementations • 26 Oct 2021 • Paul Albert, Diego Ortego, Eric Arazo, Noel O'Connor, Kevin McGuinness
We propose a simple solution to bridge the gap with a fully clean dataset using Dynamic Softening of Out-of-distribution Samples (DSOS), which we design on corrupted versions of the CIFAR-100 dataset, and compare against state-of-the-art algorithms on the web noise perturbated MiniImageNet and Stanford datasets and on real label noise datasets: WebVision 1. 0 and Clothing1M.
no code implementations • 1 Jan 2021 • Eric Arazo, Diego Ortego, Paul Albert, Noel O'Connor, Kevin McGuinness
For example, training in CIFAR-10/100 with 30% of the full training budget, a uniform sampling strategy with certain data augmentation surpasses the performance of 100% budget models trained with standard data augmentation.
1 code implementation • CVPR 2021 • Diego Ortego, Eric Arazo, Paul Albert, Noel E. O'Connor, Kevin McGuinness
We further propose a novel label noise detection method that exploits the robust feature representations learned via contrastive learning to estimate per-sample soft-labels whose disagreements with the original labels accurately identify noisy samples.
Ranked #21 on Image Classification on mini WebVision 1.0
1 code implementation • 15 Nov 2020 • Eduardo Fonseca, Diego Ortego, Kevin McGuinness, Noel E. O'Connor, Xavier Serra
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 event research.
1 code implementation • 23 Jul 2020 • Paul Albert, Diego Ortego, Eric Arazo, Noel E. O'Connor, Kevin McGuinness
We propose Reliable Label Bootstrapping (ReLaB), an unsupervised preprossessing algorithm which improves the performance of semi-supervised algorithms in extremely low supervision settings.
no code implementations • LREC 2020 • John Roberto, Diego Ortego, Brian Davis
The aim of this position paper is to establish an initial approach to the automatic classification of digital images about the Outsider Art style of painting.
1 code implementation • 18 Dec 2019 • Diego Ortego, Eric Arazo, Paul Albert, Noel E. O'Connor, Kevin McGuinness
However, we show that different noise distributions make the application of this trick less straightforward and propose to continuously relabel all images to reveal a discriminative loss against multiple distributions.
4 code implementations • 8 Aug 2019 • Eric Arazo, Diego Ortego, Paul Albert, Noel E. O'Connor, Kevin McGuinness
In the context of image classification, recent advances to learn from unlabeled samples are mainly focused on consistency regularization methods that encourage invariant predictions for different perturbations of unlabeled samples.
2 code implementations • 25 Apr 2019 • Eric Arazo, Diego Ortego, Paul Albert, Noel E. O'Connor, Kevin McGuinness
Specifically, we propose a beta mixture to estimate this probability and correct the loss by relying on the network prediction (the so-called bootstrapping loss).
Ranked #44 on Image Classification on Clothing1M
no code implementations • 25 Apr 2019 • Diego Ortego, Kevin McGuinness, Juan C. SanMiguel, Eric Arazo, José M. Martínez, Noel E. O'Connor
This guiding process relies on foreground masks from independent algorithms (i. e. state-of-the-art algorithms) to implement an attention mechanism that incorporates the spatial location of foreground and background to compute their separated representations.