Graph-based few-shot incremental learning algorithm for unknown class detection

Few-shot learning, a promising technique for acquiring new concepts from limited data, assumes that testing samples belong to “unknown classes” and are regarded as new knowledge. However, real-world scenarios introduce uncertainty about the class membership of testing samples. To address this uncertainty, we propose a novel challenge of few-shot incremental unknown class detection, aligning more closely with practical situations. Open set recognition can classify known class samples and reject unknown class samples to mitigate the uncertainty, but it struggles to address the critical limitation of having few available samples. To tackle both uncertainty and limitation, we propose a graph-based few-shot incremental learning algorithm for unknown class detection, which includes four components. First, a feature extractor learns from the base dataset during training and is subsequently fixed for embedding node features from the novel dataset during inference. Then, embedded node features, along with their corresponding prototypes, contribute to graph generation and edge construction. Third, a mixed-rejection strategy is proposed to determine the class membership of testing samples. Finally, a novel class is treated as a new known class, engaging the embedded node features in graph update and edge reconstruction. Evaluation on benchmark datasets with varying structures, including USTC-TFC2016 and miniImageNet datasets, demonstrates that our proposed algorithm outperforms classical open set recognition algorithm in few-shot incremental learning for unknown class detection, which offers promising performance and potential for practical applications in real-world scenarios.