On the generalization capabilities of FSL methods through domain adaptation: a case study in endoscopic kidney stone image classification

@inproceedings{MendezRuiz2022OnTG,
  title={On the generalization capabilities of FSL methods through domain adaptation: a case study in endoscopic kidney stone image classification},
  author={Mauricio Mendez-Ruiz and Francisco Javier Lopez-Tiro and Jonathan El Beze and Vincent Estrade and Gilberto Ochoa-Ruiz and Jacques Hubert and Andres Mendez-Vazquez and Christian Daul},
  booktitle={MICAI},
  year={2022}
}
—Deep learning has shown great promise in diverse areas of computer vision, such as image classification, object detection and semantic segmentation, among many others. However, as it has been repeatedly demonstrated, deep learning methods trained on a dataset do not generalize well to datasets from other domains or even to similar datasets, due to data distribution shifts. In this work, we propose the use of a meta-learning based few-shot learning approach to alleviate these problems. In order… 

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