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arXiv · 2306.13357

Catching Image Retrieval Generalization

Maksim Zhdanov·Ivan Karpukhin
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Abstract

The concepts of overfitting and generalization are vital for evaluating machine learning models. In this work, we show that the popular Recall@K metric depends on the number of classes in the dataset, which limits its ability to estimate generalization. To fix this issue, we propose a new metric, which measures retrieval performance, and, unlike Recall@K, estimates generalization. We apply the proposed metric to popular image retrieval methods and provide new insights about deep metric learning generalization.

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Maksim Zhdanov, Ivan Karpukhin. 2023-06-23. Catching Image Retrieval Generalization. https://arxiv.org/abs/2306.13357

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