A study on open set recognition methods - Sun, Xin (Nanyang Technological University, 2021)
Deep learning has achieved state-of-the-art performance in many computer vision tasks, but there are still multiple challenges when applying deep learning to real-world problems. One typical challenge is that incomplete knowledge exists during the training phase, and an unknown sample may be fed into the system during the testing phase, then the network will wrongly recognize it as one of the known classes. A potential solution to address this issue is Open Set Recognition (OSR), which assumes that deficient knowledge exists during the training stage, and aims to not only classify samples of known classes but detect unknown samples during the testing stage.