The Positive Impact of Metric Learning on Open Set Nearest Neighbor Classification

Abstract

Traditional machine classification problems assume that complete knowledge of all classes is available during training. However, this assumption does often not hold for fast-changing environments and safety-critical applications like self-driving cars or tumour detection. In our work, we assume an arguably more realistic scenario called open set recognition, where incomplete knowledge of all classes during training is assumed, and also unknown classes can occur during testing. More importantly, we simulate an open set scenario on four established datasets and show how Open Set Nearest Neighbor classification results can be improved with metric learning. Our results indicate that the prior application of the Large Margin Nearest Neighbor algorithm can consistently enhance the classification results and increase the ability to reject unknown instances, which is vital in scenarios of many unknown classes. These findings highlight the importance of metric learning and serve as a benchmark for further studies on the intersection between metric learning and open set recognition.