  Large-scale datasets are essential for the success of deep learning in image
retrieval. However, manual assessment errors and semi-supervised annotation
techniques can lead to label noise even in popular datasets. As previous works
primarily studied annotation quality in image classification tasks, it is still
unclear how label noise affects deep learning approaches to image retrieval. In
this work, we show that image retrieval methods are less robust to label noise
than image classification ones. Furthermore, we, for the first time,
investigate different types of label noise specific to image retrieval tasks
and study their effect on model performance.
