Examinando por Autor "Cuesta-Infante, Alfredo"

Mostrando 1 - 6 de 6

Deep reinforcement learning for automated search of model parameters: photo-fenton wastewater disinfection case study
(Springer, 2022) Hernández-García, Sergio; Cuesta-Infante, Alfredo; Moreno-SanSegundo, José Ángel; Montemayor, Antonio S.
Numerical optimization solves problems that are analytically intractable at the cost of arriving at a sufficiently good but rarely optimal solution. To maximize the result, optimization algorithms are run with the guidance and supervision of a human, usually an expert in the problem. Recent advances in deep reinforcement learning motivate interest in an artificial agent capable of learning to do the expert’s task. Specifically, we present a proximal policy optimization agent that learns to optimize in a real case study such as the modeling of the photo-fenton disinfection process, which involves a number of parameters that have to be adjusted to minimize the error of the model with respect to the experimental data collected in several trials. The expert spends an average of 4 h to find a suitable set of parameters. On the other hand, the agent we present does not require a human expert to guide or validate the optimization procedure and achieves similar results in 2:5 less time.
Pedestrian detection with LeNet-like convolutional networks
(Springer Nature, 2020-09) Cuesta-Infante, Alfredo; García, Francisco J.; Pantrigo, Juan J.; S. Montemayor, Antonio
We present a detection method that is able to detect a learned target and is valid for both static and moving cameras. As an application, we detect pedestrians, but could be anything if there is a large set of images of it. The data set is fed into a number of deep convolutional networks, and then, two of these models are set in cascade in order to filter the cutouts of a multi-resolution window that scans the frames in a video sequence. We demonstrate that the excellent performance of deep convolutional networks is very difficult to match when dealing with real problems, and yet we obtain competitive results.
Synthetic Spermatozoa Video Sequences Generation Using Adversarial Imitation Learning
(Springer, 2023-06-25) Hernández-García, Sergio; Cuesta-Infante, Alfredo; Montemayor, Antonio S.
Automated sperm sample analysis using computer vision techniques has gained increasing interest due to the tedious and time-consuming nature of manual evaluation. Deep learning models have been applied for sperm detection, tracking, motility analysis, and morphology recognition. However, the lack of labeled data hinders their adoption in laboratories. In this work, we propose a method to generate synthetic spermatozoa video sequences using Generative Adversarial Imitation Learning (GAIL). Our approach uses a parametric model based on Bezier splines to generate frames of a single spermatozoon. We evaluate our method against U-net and GAN-based approaches, and demonstrate its superior performance.
Towards Clear Evaluation of Robotic Visual Semantic Navigation
(Institute of Electrical and Electronics Engineers, 2023-05-01) Gutiérrez-Álvarez, Carlos; Hernández-García, Sergio; Nasri, Nadia; Cuesta-Infante, Alfredo; López-Sastre, Roberto J.
In this paper we address the problem of visual semantic navigation (VSN), in which a robot needs to navigate through an environment to reach an object having only access to egocentric RGB perception sensors. This is a recently explored problem, where most of the approaches leverage last advances in deep learning models for visual perception, combined with reinforcement learning (RL) strategies. Nonetheless, after a review of the literature, it is complicated to perform direct comparisons between the different solutions. The main difficulties lie in the fact that the navigation environments in which the experimental metrics are reported are not accessible, and each approach uses different RL libraries. In this paper, we release a publicly available experimental setup for the VSN problem, with the aim of providing a clear benchmark. It has been constructed using pyRIL, an open source python library for RL, and two navigation environments: Miniwolrd-Maze from gym-miniworld, and one 3D scene from HM3D dataset using AI Habitat simulator. We finally propose a state-of-the-art VSN model, consisting in a Contrastive Language Image Pretraining (CLIP) visual encoder plus a set of two recurrent neural networks for producing the discrete navigation actions. This model is evaluated in the proposed experimental setup, with a careful analysis of the main VSN challenges, namely: the sparse rewards problem; and the exploitation-exploration trade-off. Code is available at: https://github.com/gramuah/vsn.
Towards Reducing Biases in Combining Multiple Experts Online
(International Joint Conferences on Artificial Intelligence, 2021) Sun, Yi; Ramírez Díaz, Iván; Cuesta-Infante, Alfredo; Veeramachaneni, Kalyan
In many real life situations, including job and loan applications, gatekeepers must make justified and fair real-time decisions about a person’s fitness for a particular opportunity. In this paper, we aim to accomplish approximate group fairness in an online stochastic decision-making process, where the fairness metric we consider is equalized odds. Our work follows from the classical learning-fromexperts scheme, assuming a finite set of classifiers (human experts, rules, options, etc) that cannot be modified. We run separate instances of the algorithm for each label class as well as sensitive groups, where the probability of choosing each instance is optimized for both fairness and regret. Our theoretical results show that approximately equalized odds can be achieved without sacrificing much regret. We also demonstrate the performance of the algorithm on real data sets commonly used by the fairness community.
Visual classification of dumpsters with capsule networks
(ACS, 2022) Garcia-Espinosa, Francisco J.; Concha, David; Pantrigo, Juan J.; Cuesta-Infante, Alfredo
Garbage management is an essential task in the everyday life of a city. In many countries, dumpsters are owned and deployed by the public administration. An updated what-and-where list is in the core of the decision making process when it comes to remove or renew them. Moreover, it may give extra information to other analytics in a smart city context. In this paper, we present a capsule network-based architecture to automate the visual classification of dumpsters. We propose different network hyperparameter settings, such as reducing convolutional kernel size and increasing convolution layers. We also try several data augmentation strategies, as crop and flip image transformations. We succeed in reducing the number of network parameters by 85% with respect to the best previous method, thus decreasing the required training time and making the whole process suitable for low cost and embedded software architectures. In addition, the paper provides an extensive experimental analysis including an ablation study that illustrates the contribution of each component in the proposed method. Our proposal is compared with the state-of-the-art method, which is based on a Google Inception V3 architecture pretrained with Imagenet. Experimental results show that our proposal achieves a 95.35% accuracy, 2.35% over the previous best method.