# Transfer Learning for Illustration Classification Source: [Transfer Learning for Illustration Classification]() Collection index: [llms.txt]() Data updated: 2025-10-25T21:24:52.728Z Authors: Manuel Lagunas, Elena Garces Published: 2018-05-23T09:06:16.000Z ## Abstract The field of image classification has shown an outstanding success thanks to the development of deep learning techniques. Despite the great performance obtained, most of the work has focused on natural images ignoring other domains like artistic depictions. In this paper, we use transfer learning techniques to propose a new classification network with better performance in illustration images. Starting from the deep convolutional network VGG19, pre-trained with natural images, we propose two novel models which learn object representations in the new domain. Our optimized network will learn new low-level features of the images (colours, edges, textures) while keeping the knowledge of the objects and shapes that it already learned from the ImageNet dataset. Thus, requiring much less data for the training. We propose a novel dataset of illustration images labelled by content where our optimized architecture achieves $\\textbf{86.61\\%}$ of top-1 and $\\textbf{97.21\\%}$ of top-5 precision. We additionally demonstrate that our model is still able to recognize objects in photographs. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. ## Original sources and implementation [Original paper]() [PDF]()