Deep learning has been intensively researched in computer vision tasks like im-age classification. Collecting and labeling images that these neural networks aretrained on is labor-intensive, which is why alternative methods of collecting im-ages are of interest. Virtual environments allow rendering images and automaticlabeling, which could speed up the process of generating training data and re-duce costs.This thesis studies the problem of transfer learning in image classificationwhen the classifier has been trained on rendered images using a game engine andtested on real images. The goal is to render images using a game engine to createa classifier that can separate images depicting people wearing civilian clothingor camouflage. The thesis also studies how domain adaptation techniques usinggenerative adversarial networks could be used to improve the performance ofthe classifier. Experiments show that it is possible to generate images that canbe used for training a classifier capable of separating the two classes. However,the experiments with domain adaptation were unsuccessful. It is instead recom-mended to improve the quality of the rendered images in terms of features usedin the target domain to achieve better results.
| Identifer | oai:union.ndltd.org:UPSALLA1/oai:DiVA.org:liu-165758 |
| Date | January 2019 |
| Creators | Thornström, Johan |
| Publisher | Linköpings universitet, Datorseende |
| Source Sets | DiVA Archive at Upsalla University |
| Language | English |
| Detected Language | English |
| Type | Student thesis, info:eu-repo/semantics/bachelorThesis, text |
| Format | application/pdf |
| Rights | info:eu-repo/semantics/openAccess |
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