Deep feature learning for image classification via countering over-fitting - Qing, Yuanyuan (Nanyang Technological University, 2021)
The great success of deep neural networks on visual recognition has inspired numerous real-world applications. However, such superior performance is closely related to model complexity and the amount of annotated data. Over-deepened networks and lack of data annotation will degrade generalization capability of the model as over-fitting problems arise. In this thesis, the focus is on extracting robust semantic features in image data by alleviating over-fitting problems under different learning frameworks.