The Art of the Masses: Overviews on the Collective Visual Heritage through Convolutional Neural Networks

Abstract

In the context of a society saturated in images, convolutional neural networks (CNNs), pre-trained using from the visual information contained in many thousands of images, constitute a tool that is of great use in helping us to organize the visual heritage, thus offering a route of entry that would otherwise be impossible. One of the responsibilities of the contemporary artist is to adopt a position that will help to provide sense, to project meaning onto the accumulation of images that we are faced with. The artificial neuronal network ResNet-50 has been used in order to extract the visual characteristics of large sets of images from the internet. Textual searches have been carried out on social issues such as climate change, the COVID-19 pandemic, demonstrations around the world, and manifestations of popular culture, and the image descriptors obtained have been the input for the algorithm t-SNE. In this way, we produce large visual maps composed of thousands of images and arranged following the criteria of formal similitude, displaying the visual patterns of the archetypes of specific semantic categories. The method of filing and recovering our collective memory must have a correlation with the technological and scientific advances of our time, in order for us to progressively discover new horizons of knowledge.

Keywords

• deep learning
• post-photography
• computer vision
• t-SNE
• convolutional neural networks (CNNs)
• Aby Warburg