Learning with Label Noise for Image Retrieval by Selecting Interactions

• URL: http://arxiv.org/abs/2112.10453v2
• Date: Tue, 21 Dec 2021 12:41:39 GMT
• Title: Learning with Label Noise for Image Retrieval by Selecting Interactions
• Authors: Sarah Ibrahimi and Arnaud Sors and Rafael Sampaio de Rezende and St\'ephane Clinchant
• Abstract summary: We propose a noise-resistant method for image retrieval named Teacher-based Selection of Interactions, T-SINT. It selects correct positive and negative interactions to be considered in the retrieval loss by using a teacher-based training setup. It consistently outperforms state-of-the-art methods on high noise rates across benchmark datasets with synthetic noise and more realistic noise.
• Score: 2.0881411175861726
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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