Classification of offensive game-emblem drawings using CNN (convolutional neural networks) and transfer learning

Sammanfattning: Convolutional neural networks (CNN) has become an important tool to solve many computer vision tasks of today. The technique is though costly, and training a network from scratch requires both a large dataset and adequate hardware. A solution to these shortcomings is to instead use a pre-trained network, an approach called transfer learning. Several studies have shown promising results applying transfer learning, but the technique requires further studies. This thesis explores the capabilities of transfer learning when applied to the task of filtering out offensive cartoon drawings in the game of Battlefield 1. GoogLeNet was pre-trained on ImageNet, and then the last layers were fine-tuned towards the target task and domain. The model achieved an accuracy of 96.71% when evaluated on the binary classification task of predicting non-offensive or swastika/penis content in Battlefield "emblems". The results indicate that a CNN trained on ImageNet is applicable, even when the target domain is very different from the pre-trained networks domain.