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RoNFA: Robust Neural Field-based Approach for Few-Shot Image Classification with Noisy Labels
In few-shot learning (FSL), the labeled samples are scarce. Thus, label errors can significantly reduce classification accuracy. Since label errors are inevitable in realistic learning tasks, improving the robustness of the model in the presence of label errors is critical. This paper proposes a new
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