In the case in which few-shot learning is done through data augmentation, the impact might be less likely to be significant, as the structure of the model, the initialization of parameters, the hypothesis space, and the amount of information used during model training are mostly identical to a non-few-shot setting.
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Illuminating the Black Box: Interpreting Deep Neural Network Models for Psychiatric Research.
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