Zobrazeno 1 - 10
of 87
pro vyhledávání: '"García Gasulla, Darío"'
Autor:
Tormos, Adrian, Llauradó, Blanca, Núñez, Fernando, Romero, Axel, Garcia-Gasulla, Dario, Béjar, Javier
The scarcity of data in medical domains hinders the performance of Deep Learning models. Data augmentation techniques can alleviate that problem, but they usually rely on functional transformations of the data that do not guarantee to preserve the or
Externí odkaz:
http://arxiv.org/abs/2411.18926
Large Language Models (LLMs) have demonstrated remarkable capabilities in natural language processing, and yet, their factual inaccuracies and hallucinations limits their application, particularly in critical domains like healthcare. Context retrieva
Externí odkaz:
http://arxiv.org/abs/2409.15127
The continued release of increasingly realistic image generation models creates a demand for synthetic image detectors. To build effective detectors we must first understand how factors like data source diversity, training methodologies and image alt
Externí odkaz:
http://arxiv.org/abs/2409.14128
Convolutional layers are a fundamental component of most image-related models. These layers often implement by default a static padding policy (\eg zero padding), to control the scale of the internal representations, and to allow kernel activations c
Externí odkaz:
http://arxiv.org/abs/2309.08048
Autor:
Urcelay, Lucia, Hinjos, Daniel, Martin-Torres, Pablo A., Gonzalez, Marta, Mendez, Marta, Cívico, Salva, Álvarez-Napagao, Sergio, Garcia-Gasulla, Dario
In Vitro Fertilization is among the most widespread treatments for infertility. One of its main challenges is the evaluation and selection of embryo for implantation, a process with large inter- and intra-clinician variability. Deep learning based me
Externí odkaz:
http://arxiv.org/abs/2308.02534
In deep learning, transfer learning (TL) has become the de facto approach when dealing with image related tasks. Visual features learnt for one task have been shown to be reusable for other tasks, improving performance significantly. By reusing deep
Externí odkaz:
http://arxiv.org/abs/2211.04347
Autor:
Gnatyshak, Dmitry, Garcia-Gasulla, Dario, Alvarez-Napagao, Sergio, Arjona, Jamie, Venturini, Tommaso
Studying misinformation and how to deal with unhealthy behaviours within online discussions has recently become an important field of research within social studies. With the rapid development of social media, and the increasing amount of available i
Externí odkaz:
http://arxiv.org/abs/2203.11261
AI explainability improves the transparency of models, making them more trustworthy. Such goals are motivated by the emergence of deep learning models, which are obscure by nature; even in the domain of images, where deep learning has succeeded the m
Externí odkaz:
http://arxiv.org/abs/2109.15035
Today, Artificial Intelligence (AI) has a direct impact on the daily life of billions of people. Being applied to sectors like finance, health, security and advertisement, AI fuels some of the biggest companies and research institutions in the world.
Externí odkaz:
http://arxiv.org/abs/2009.13871
Autor:
Parés, Ferran, Arias-Duart, Anna, Garcia-Gasulla, Dario, Campo-Francés, Gema, Viladrich, Nina, Ayguadé, Eduard, Labarta, Jesús
In the image classification task, the most common approach is to resize all images in a dataset to a unique shape, while reducing their precision to a size which facilitates experimentation at scale. This practice has benefits from a computational pe
Externí odkaz:
http://arxiv.org/abs/2007.13693