Improving the trustworthiness of image classification models by utilizing bounding-box annotations
This work addresses trustworthiness issues for users of image classification models, but it appears incremental as it builds on existing datasets and methods.
The paper tackled improving trustworthiness in image classification by incorporating bounding-box annotations into the training objective, resulting in better performance in accuracy, robustness, and interpretability compared to baselines.
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.