Prototypical Mixing and Retrieval-based Refinement for Label Noise-resistant Image Retrieval

Label noise is pervasive in real-world applications, which influences the optimization of neural network models. This paper investigates a realistic but understudied problem of image retrieval under label noise, which could lead to severe overfitting or memorization of noisy samples during optimization. Moreover, identifying noisy samples correctly is still a challenging problem for retrieval models. In this paper, we propose a novel approach called Prototypical Mixing and Retrieval-based Refinement (TI-TAN) for label noise-resistant image retrieval, which corrects label noise and mitigates the effects of the memorization simultaneously. Specifically, we first characterize numerous prototypes with Gaussian distributions in the hidden space, which would direct the Mixing procedure in providing synthesized samples. These samples are fed into a similarity learning framework with varying emphasis based on the prototypical structure to learn semantics with reduced overfitting. In addition, we retrieve comparable samples for each prototype from simple to complex, which refine noisy samples in an accurate and class-balanced manner. Comprehensive experiments on five benchmark datasets demonstrate the superiority of our proposed TITAN compared with various competing baselines.