No abstract

This study uses computer vision models, which to some extent simulate the initial stages of human visual perception, to help categorize data in large sets of images of artworks by the artist Antoni Tàpies. The images have been analyzed on the basis of their compositional, chromatic and organizational characteristics, without textual notes, so that the analogies found may take us closer to, and help us to understand, the creator’s original values. The system as programmed can assist the specialist by establishing analogies between different artists or periods using the same criteria.

Generative adversarial networks (GANs) provide powerful architectures for deep generative learning. GANs have enabled us to achieve an unprecedented degree of realism in the creation of synthetic images of human faces, landscapes, and buildings, among others. Not only image generation, but also image manipulation is possible with GANs. Generative deep learning models are inherently limited in their creative abilities because of a focus on learning for perfection. We investigated the potential of GAN’s latent spaces to encode human expressions, highlighting creative interest for suboptimal solutions rather than perfect reproductions, in pursuit of the artistic concept. We have trained Deep Convolutional GAN (DCGAN) and StyleGAN using a collection of portraits of detained persons, portraits of dead people who died of violent causes, and people whose portraits were taken during an orgasm. We present results which diverge from standard usage of GANs with the specific intention of producing portraits that may assist us in the representation and recognition of otherness in contemporary identity construction.

Abstract. We have approached the difficulties of automatic cataloguing of images on which the conception and design of sculptor M. Planas artistic production are based. In order to build up a visual vocabulary for basing image description on, we followed a procedure similar to the method Bag-of-Words (BOW). We have implemented a probabilistic latent semantic analysis (PLSA) that detects underlying topics in images. Whole image collection was clustered into different types that describe aesthetic preferences of the artist. The outcomes are promising, the described cataloguing method may provide new viewpoints for the artist in future works.

Esta semana hablamos con Pilar Rosado. Pilar ha publicado diversos ensayos sobre la aplicación de modelos de visión por computador para el análisis de grandes colecciones de imágenes de arte abstracto, proporcionando puntos de vista alternativos para la reflexión acerca de las convenciones de nuestra mirada. En su práctica artística explora cuestiones políticas que se pueden abordar desde la imagen y que implican a las tecnologías de aprendizaje automático, como la gestión de la información en los archivos visuales del futuro, la revisión de la memoria colectiva o la creatividad artificial.