Material Artefact Generation

Even in the age of big data, some high-quality data sets of images with distinct artefacts are hard to obtain, either due to the source being limited or due to annotation difficulty. This applies for multiple areas such as engineering and radiology. Current state of the art methods for classification and defect detection require large well-balanced data sets to be feasible. For small data sets we face the issue of overfitting and data paucity which cause models to misclassify data in favor of over-represented classes. Part of this work deals with finding a suitable method for generating realistic images for given datasets and experiments with mitigating overfitting and data paucity by generating new images based the original dataset using Conditional Generative Adversarial Networks (CGAN) and heuristic annotation generator. Three datasets were used overall during this project. Threads dataset was used during the image generation experiments phase due to it's complexity in structure. Subsequently, two additional CGAN networks have been trained, one for Ceramics and the other for brain scans from BraTS dataset. Ceramics and BraTS was later used to evaluate the effect of generated data on training classification and segmentation networks.

Synthetic data generation, generative adversarial networks, convolutional neural networks, classification, segmentation.