  In Few-Shot Learning (FSL), models are trained to recognise unseen objects
from a query set, given a few labelled examples from a support set. In standard
FSL, models are evaluated on query instances sampled from the same class
distribution of the support set. In this work, we explore the more nuanced and
practical challenge of Open-Set Few-Shot Recognition (OSFSL). Unlike standard
FSL, OSFSL incorporates unknown classes into the query set, thereby requiring
the model not only to classify known classes but also to identify outliers.
Building on the groundwork laid by previous studies, we define a novel
transductive inference technique that leverages the InfoMax principle to
exploit the unlabelled query set. We called our approach the Enhanced Outlier
Logit (EOL) method. EOL refines class prototype representations through model
calibration, effectively balancing the inlier-outlier ratio. This calibration
enhances pseudo-label accuracy for the query set and improves the optimisation
objective within the transductive inference process. We provide a comprehensive
empirical evaluation demonstrating that EOL consistently surpasses traditional
methods, recording performance improvements ranging from approximately $+1.3%$
to $+6.3%$ across a variety of classification and outlier detection metrics and
benchmarks, even in the presence of inlier-outlier imbalance.
