… Noisy few-shot results We compare all our proposed methods for noisy fewshot learning–Median,
Absolute, Euclidean, Cosine, and TraNFS–with several baselines (see Supp. for …

… In our work, we study an extension of few-shot learning where more novel class
instances are available, reducing the risk of overfitting when fine-tuning the model. Metric …

… fewshot evaluation procedure robust to noisy support examples of novel classes, we compare
our method with two popular full-shot noisy label training methods in our setting (Sec. 5.9). …

… for few-shot learning with noisy labels. The model contains a feature extraction module
based on an attention mechanism and a comprehensive multi-step noise detection module. …

… In this paper, we unify the setting of noisy label FSL … few-shot learning (OFSL) setting
where labels of support sets are unreliable and some samples are randomly polluted by noisy …

… label noises from support samples, where every sample is of great value in characterizing the
few-shot … Graph convolutional networks for learning with few clean and many noisy labels. …

… We interpret this problem as learning with noisy labels, leveraging … learning with noisy labels,
it is common to detect noisy labels based on statistics of this loss for clean and noisy labels […

… often results in label noise. We present a method for learning from noisy labels that leverages
… propagation within stochastic gradient descent for episodic few-shot learning [39]. This is a …

… few-shot transfer to new tasks, whereas we aim to learn a … transfer labels among neighbors,
the synthetic noisy labels are … Optimization as a model for few-shot learning. In ICLR, 2017. …

… C, we investigate noisy few-shot performance for different numbers of shots from the 5-shot
set… performance of few-shot learning methods within the context of support set noise primarily …