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Modelos Generativos basados en Mecanismos de Difusión
In the image case, a progressive pixel corruption process is carried out by applying random noise, and a neural network is trained to revert each one of the corruption steps.
Autocodificadores Variacionales (VAE) Fundamentos Teóricos y Aplicaciones
VAEs are probabilistic graphical models based on neural networks that allow the coding of input data in a latent space formed by simpler probability distributions and the reconstruction, based on such latent variables, of the source data.
Redes Generativas Adversarias (GAN) Fundamentos Teóricos y Aplicaciones
Generative models model the probability distribution of a data set, but instead of providing a probability value, they generate new instances that are close to the original distribution.
Transformadores: Fundamentos teoricos y Aplicaciones
Transformers are a neural network architecture originally designed for natural language processing that it is now a mainstream tool for solving a wide variety of problems, including natural language processing, sound, image, reinforcement learning, and other problems with heterogeneous input data.
Identification and Visualization of the Underlying Independent Causes of the Diagnostic of Diabetic Retinopathy made by a Deep Learning Classifier
In this paper we go forward into the generation of explanations by identifying the independent causes that use a deep learning model for classifying an image into a certain class.
A Deep Learning Interpretable Classifier for Diabetic Retinopathy Disease Grading
Deep neural network models have been proven to be very successful in image classification tasks, also for medical diagnosis, but their main concern is its lack of interpretability.
