2026-04-17T05:43:21Zhttps://recercat.cat/oai/request

oai:recercat.cat:2117/998102025-07-23T00:33:34Zcom_2072_1033col_2072_452951

urn:hdl:2117/99810 Convolutional neural networks for style classification Oliver, Philipp Àrees temàtiques de la UPC::Informàtica::Intel·ligència artificial Neural networks (Computer science) Artificial intelligence Artificial Intelligence Convolutional Neural Feature Map Correlations Gram Matrix Classification Style Art Flickr Wikipaintings Xarxes neuronals (Informàtica) Intel·ligència artificial Amb la col·laboració d'aquestes universitats: UNIVERSITAT DE BARCELONA UNIVERSITAT ROVIRA I VIRGILI In recent years convolutional neural networks have enjoyed great success. Especially in the field of object recognition great leaps forward have been made. Researchers were able to exploit the object detection features from such networks for many useful and interesting applications like sentiment analysis and information retrieval. Unfortunately, many times the importance of style is not being considered adequately in these systems. This is partly because style is a concept that is difficult to define and labeled data is scarce. Recent developments in texture synthesis and style transfer, however, sparked new interest in the field. In particular feature correlations from convolutional neural networks, which were trained on object recognition, have been shown to work well on these tasks. I propose that such techniques can help in classifying style. In the course of this thesis I setup a experiment to show that this is indeed the case. Furthermore, I show that the performance of the CNN and the depth of the layer from which the feature correlations are taken from influences the classification performance. 2016-06-28 Master thesis Open Access Universitat Politècnica de Catalunya