  Learning with noisy labels is an active research area for image
classification. However, the effect of noisy labels on image retrieval has been
less studied. In this work, we propose a noise-resistant method for image
retrieval named Teacher-based Selection of Interactions, T-SINT, which
identifies noisy interactions, ie. elements in the distance matrix, and selects
correct positive and negative interactions to be considered in the retrieval
loss by using a teacher-based training setup which contributes to the
stability. As a result, it consistently outperforms state-of-the-art methods on
high noise rates across benchmark datasets with synthetic noise and more
realistic noise.
