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Improving the trustworthiness of image classification models by utilizing bounding-box annotations

2021-08-15Code Available0· sign in to hype

Dharma KC, Chicheng Zhang

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Abstract

We study utilizing auxiliary information in training data to improve the trustworthiness of machine learning models. Specifically, in the context of image classification, we propose to optimize a training objective that incorporates bounding box information, which is available in many image classification datasets. Preliminary experimental results show that the proposed algorithm achieves better performance in accuracy, robustness, and interpretability compared with baselines.

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