# Deep Image Retrieval is not Robust to Label Noise

Source: [Deep Image Retrieval is not Robust to Label Noise](<https://www.sota2.com/research/paper/68fa6c981e209131cea34c0d>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-26T00:13:46.432Z

Authors: Stanislav Dereka, Ivan Karpukhin, Sergey Kolesnikov

Published: 2022-05-23T11:04:09.000Z

## Abstract

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.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2205.11195>)

[PDF](<https://arxiv.org/pdf/2205.11195.pdf>)