In this paper, we attack a few-shot open-set recognition (FSOSR) problem,
which is a combination of few-shot learning (FSL) and open-set recognition
(OSR). It aims to quickly adapt a model to a given small set of labeled samples
while rejecting unseen class samples. Since OSR requires rich data and FSL
considers closed-set classification, existing OSR and FSL methods show poor
performances in solving FSOSR problems. The previous FSOSR method follows the
pseudo-unseen class sample-based methods, which collect pseudo-unseen samples
from the other dataset or synthesize samples to model unseen class
representations. However, this approach is heavily dependent on the composition
of the pseudo samples. In this paper, we propose a novel unknown class sample
detector, named SnaTCHer, that does not require pseudo-unseen samples. Based on
the transformation consistency, our method measures the difference between the
transformed prototypes and a modified prototype set. The modified set is
composed by replacing a query feature and its predicted class prototype.
SnaTCHer rejects samples with large differences to the transformed prototypes.
Our method alters the unseen class distribution estimation problem to a
relative feature transformation problem, independent of pseudo-unseen class
samples. We investigate our SnaTCHer with various prototype transformation
methods and observe that our method consistently improves unseen class sample
detection performance without closed-set classification reduction.

探讨了在少量标注样本的情况下，如何通过提出的样本识别器 SnaTCHer，将未知类别的样本进行有效区分，以提高未知类别样本的检测性能。