A soft K-means prototype classifier with adaptive receptive fields is claimed to keep near-perfect few-shot accuracy under 60% label noise, but the paper omits code, hyperparameters, and pretraining disclosure.
Me-momentum: Extracting hard confident examplesfromnoisilylabeleddata,in:ProceedingsoftheIEEE/CVF International Conference on Computer Vision (ICCV), pp
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RoNFA: Robust Neural Field-based Approach for Few-Shot Image Classification with Noisy Labels
A soft K-means prototype classifier with adaptive receptive fields is claimed to keep near-perfect few-shot accuracy under 60% label noise, but the paper omits code, hyperparameters, and pretraining disclosure.