  We tackle the Few-Shot Open-Set Recognition (FSOSR) problem, i.e. classifying
instances among a set of classes for which we only have few labeled samples,
while simultaneously detecting instances that do not belong to any known class.
Departing from existing literature, we focus on developing model-agnostic
inference methods that can be plugged into any existing model, regardless of
its architecture or its training procedure. Through evaluating the embedding's
quality of a variety of models, we quantify the intrinsic difficulty of
model-agnostic FSOSR. Furthermore, a fair empirical evaluation suggests that
the naive combination of a kNN detector and a prototypical classifier ranks
before specialized or complex methods in the inductive setting of FSOSR. These
observations motivated us to resort to transduction, as a popular and practical
relaxation of standard few-shot learning problems. We introduce an Open Set
Transductive Information Maximization method OSTIM, which hallucinates an
outlier prototype while maximizing the mutual information between extracted
features and assignments. Through extensive experiments spanning 5 datasets, we
show that OSTIM surpasses both inductive and existing transductive methods in
detecting open-set instances while competing with the strongest transductive
methods in classifying closed-set instances. We further show that OSTIM's model
agnosticity allows it to successfully leverage the strong expressive abilities
of the latest architectures and training strategies without any hyperparameter
modification, a promising sign that architectural advances to come will
continue to positively impact OSTIM's performances.
