Abstract
This paper proposes a technique for training a neural network by minimizing a surrogate loss that approximates the target evaluation metric, which may be non-differentiable. The surrogate is learned via a deep embedding where the Euclidean distance between the prediction and the ground truth corresponds to the value of the evaluation metric. The effectiveness of the proposed technique is demonstrated in a post-tuning setup, where a trained model is tuned using the learned surrogate. Without a significant computational overhead and any bells and whistles, improvements are demonstrated on challenging and practical tasks of scene-text recognition and detection. In the recognition task, the model is tuned using a surrogate approximating the edit distance metric and achieves up to 39%\documentclass[12pt]{minimal}
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\begin{document}$$39\%$$\end{document} relative improvement in the total edit distance. In the detection task, the surrogate approximates the intersection over union metric for rotated bounding boxes and yields up to 4.25%\documentclass[12pt]{minimal}
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\begin{document}$$4.25\%$$\end{document} relative improvement in the F1\documentclass[12pt]{minimal}
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\begin{document}$$F_{1}$$\end{document} score.