text: 107,184,1453527034483941377,764045672,Mayank Agarwal,"Delighted to share our upcoming #NeurIPS2021 paper ""On sensitivity of meta-learning to support data""(<LINK>) We study the sensitivity of meta-learning methods to adaptation data, and show the existence of... (1/2) <LINK> unaltered, in-distribution, and natural images that, when used for adaptation, yield accuracy as low as 4% or as high as 95% on standard few-shot image classification benchmarks. (2/2) @icepieces Thank you Yas!",https://arxiv.org/abs/2110.13953,"Meta-learning algorithms are widely used for few-shot learning. For example, image recognition systems that readily adapt to unseen classes after seeing only a few labeled examples. Despite their success, we show that modern meta-learning algorithms are extremely sensitive to the data used for adaptation, i.e. support data. In particular, we demonstrate the existence of (unaltered, in-distribution, natural) images that, when used for adaptation, yield accuracy as low as 4\% or as high as 95\% on standard few-shot image classification benchmarks. We explain our empirical findings in terms of class margins, which in turn suggests that robust and safe meta-learning requires larger margins than supervised learning. ",On sensitivity of meta-learning to support data,3,"['Delighted to share our upcoming #NeurIPS2021 paper ""On sensitivity of meta-learning to support data""(<LINK>)\n\nWe study the sensitivity of meta-learning methods to adaptation data, and show the existence of... (1/2) <LINK>', 'unaltered, in-distribution, and natural images that, when used for adaptation, yield accuracy as low as 4% or as high as 95% on standard few-shot image classification benchmarks. (2/2)', '@icepieces Thank you Yas!']",21,10,431