Open Access
Capas basadas en operadores OWA para Redes Neuronales Convolucionales
(2020) Domínguez Catena, Iris; Galar Idoate, Mikel; Paternain Dallo, Daniel; Escuela Técnica Superior de Ingeniería Industrial, Informática y de Telecomunicación; Industria, Informatika eta Telekomunikazio Ingeniaritzako Goi Mailako Eskola Teknikoa
En este trabajo exploramos una nueva forma de ampliar la capacidad de las Redes Neuronales Convolucionales. En concreto, planteamos una nueva t´ecnica para generar informaci´on adicional a partir de la salida de un bloque convolucional de una Red Neuronal Convolucional, empleando para ello operadores OWA a nivel de canal, y usando esta nueva informaci´on para ampliar la entrada de las siguientes capas de la red. Realizamos diversas pruebas con esta nueva t´ecnica, comprobando como afectan diferentes par´ametros a los resultados, incluyendo el punto de inserci´on de la nueva informaci´on, la cantidad de operadores OWA aplicados, o el tipo de m´etrica empleada para ordenar los canales de informaci´on original.
Open Access
DSAP: analyzing bias through demographic comparison of datasets
(Elsevier, 2024-10-29) Domínguez Catena, Iris; Paternain Dallo, Daniel; Galar Idoate, Mikel; Estadística, Informática y Matemáticas; Estatistika, Informatika eta Matematika; Universidad Publica de Navarra / Nafarroako Unibertsitate Publikoa ; Gobierno de Navarra / Nafarroako Gobernua
In the last few years, Artificial Intelligence (AI) systems have become increasingly widespread. Unfortunately, these systems can share many biases with human decision-making, including demographic biases. Often, these biases can be traced back to the data used for training, where large uncurated datasets have become the norm. Despite our awareness of these biases, we still lack general tools to detect, quantify, and compare them across different datasets. In this work, we propose DSAP (Demographic Similarity from Auxiliary Profiles), a two-step methodology for comparing the demographic composition of datasets. First, DSAP uses existing demographic estimation models to extract a dataset's demographic profile. Second, it applies a similarity metric to compare the demographic profiles of different datasets. While these individual components are well-known, their joint use for demographic dataset comparison is novel and has not been previously addressed in the literature. This approach allows three key applications: the identification of demographic blind spots and bias issues across datasets, the measurement of demographic bias, and the assessment of demographic shifts over time. DSAP can be used on datasets with or without explicit demographic information, provided that demographic information can be derived from the samples using auxiliary models, such as those for image or voice datasets. To show the usefulness of the proposed methodology, we consider the Facial Expression Recognition task, where demographic bias has previously been found. The three applications are studied over a set of twenty datasets with varying properties. The code is available at https://github.com/irisdominguez/DSAP.
Open Access
Learning channel-wise ordered aggregations in deep neural networks
(Springer, 2021) Domínguez Catena, Iris; Paternain Dallo, Daniel; Galar Idoate, Mikel; Institute of Smart Cities - ISC; Universidad Pública de Navarra / Nafarroako Unibertsitate Publikoa
One of the most common techniques for approaching image classification problems are Deep Neural Networks. These systems are capable of classifying images with different levels of detail at different levels of detail, with an accuracy that sometimes can surpass even manual classification by humans. Most common architectures for Deep Neural Networks are based on convolutional layers, which perform at the same time a convolution on each input channel and a linear aggregation on the convoluted channels. In this work, we develop a new method for augmenting the information of a layer inside a Deep Neural Network using channel-wise ordered aggregations. We develop a new layer that can be placed at different points inside a Deep Neural Network. This layer takes the feature maps of the previous layer and adds new feature maps by applying several channel-wise ordered aggregations based on learned weighting vectors. We perform several experiments introducing this layer in a VGG neural network and study the impact of the new layer, obtaining better accuracy scores over a sample dataset based on ImageNet. We also study the convergence and evolution of the weighting vectors of the new layers over the learning process, which gives a better understanding of the way the system is exploiting the additional information to gain new knowledge.

Domínguez Catena, Iris

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