Few-Shot Learning with Deep Neural Networks for Visual Quality Control: Evaluations on a Production Line

Abstract: Having a well representative and adequate amount of data samples plays an important role in the success of deep learning algorithms used for image recognition. On the other hand, collecting and manually labeling a large-scale dataset requires a great deal of human interaction which in turn is very timeconsuming. In this thesis project, we explore the possibilities of new deeplearning approaches used for image recognition that do not require a big amount of data. Since Few-Shot Learning (FSL) models are known to be the most promising approach to tackle the problem of not having an adequate dataset, a hand full of the state-of-the-art algorithms based on FSL approach such as Model-Agnostic Meta-Learning (MAML), Prototypical Networks (ProtoNet), Relation Networks (RelationNet), Baseline, and Baseline++ are implemented and analyzed. These models are used to classify a series of issues for the automation of the visual quality inspection in a production line. Moreover, the performance of the deeper networks in comparison to the shallower networks is explored. Our experiment results on the available dataset show that the Baseline++ model has the best performance among the other models. Furthermore, Baseline++ with a six-layer convolutional network as a feature backbone is a relatively simple model to train that does not require a high computational power compared to the other models.