A bi-level metric learning framework via self-paced learning weighting

Distance metric learning (DML) has achieved great success in many real-world applications. However, most existing DML models characterize the quality of tuples on the tuple level while ignoring the an-chor level. Therefore, the models are less accurate to portray the quality of tuples and tend to be over -fitting when anchors are noisy samples. In this paper, we devise a bi-level metric learning framework (BMLF), which characterizes the quality of tuples more finely on both levels, enhancing the generaliza-tion performance of the DML model. Furthermore, we present an implementation of BMLF based on a self-paced learning regular term and design the corresponding optimization algorithm. By weighing tu-ples on the anchor level and training the model using tuples with higher weights preferentially, the side effect of low-quality noisy samples will be alleviated. We empirically demonstrate that the effectiveness and robustness of the proposed method outperform the state-of-the-art methods on several benchmark datasets. © 2023 Elsevier Ltd. All rights reserved.