198 citations · 203 across the 7 of their papers we have counts for
12 papers
LEA: Improving Sentence Similarity Robustness to Typos Using Lexical Attention Bias
Mario Almagro, Emilio Almazán, Diego Ortego +1
Textual noise, such as typos or abbreviations, is a well-known issue that penalizes vanilla Transformers for most downstream tasks. We show that this is also the case for sentence…
Block-SCL: Blocking Matters for Supervised Contrastive Learning in Product Matching
Mario Almagro, David Jiménez, Diego Ortego +2
Product matching is a fundamental step for the global understanding of consumer behavior in e-commerce. In practice, product matching refers to the task of deciding if two product…
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…
Towards Robust Learning with Different Label Noise Distributions
Diego Ortego, Eric Arazo, Paul Albert +2
Noisy labels are an unavoidable consequence of labeling processes and detecting them is an important step towards preventing performance degradations in Convolutional Neural Networ…
Pseudo-Labeling and Confirmation Bias in Deep Semi-Supervised Learning
Eric Arazo, Diego Ortego, Paul Albert +2
Semi-supervised learning, i.e. jointly learning from labeled and unlabeled samples, is an active research topic due to its key role on relaxing human supervision. In the context of…
On guiding video object segmentation
Diego Ortego, Kevin McGuinness, Juan C. SanMiguel +3
This paper presents a novel approach for segmenting moving objects in unconstrained environments using guided convolutional neural networks. This guiding process relies on foregrou…
Unsupervised Label Noise Modeling and Loss Correction
Eric Arazo, Diego Ortego, Paul Albert +2
Despite being robust to small amounts of label noise, convolutional neural networks trained with stochastic gradient methods have been shown to easily fit random labels. When there…