We tackle the Few-Shot Open-Set Recognition (FSOSR) problem, i.e. classifying instances among a set of classes for which we only have a few labeled samples, while simultaneously detecting instances that do not belong to any known class. We explore the popular transductive setting, which leverages the unlabelled query instances at inference. Motivated by the observation that existing transductive methods perform poorly in open-set scenarios, we propose a generalization of the maximum likelihood principle, in which latent scores down-weighing the influence of potential outliers are introduced alongside the usual parametric model. Our formulation embeds supervision constraints from the support set and additional penalties discouraging overconfident predictions on the query set. We proceed with a block-coordinate descent, with the latent scores and parametric model co-optimized alternately, thereby benefiting from each other. We call our resulting formulation \textit{Open-Set Likelihood Optimization} (OSLO). OSLO is interpretable and fully modular; it can be applied on top of any pre-trained model seamlessly. Through extensive experiments, we show that our method surpasses existing inductive and transductive methods on both aspects of open-set recognition, namely inlier classification and outlier detection.

翻译：我们解决小样本开集识别（FSOSR）问题，即对仅有少量标注样本的类别集合进行实例分类，同时检测不属于任何已知类别的实例。本文探索了流行的传导式设置，该设置在推理阶段利用未标注的查询实例。受现有传导方法在开集场景下表现不佳的观察启发，我们提出了一种极大似然原理的泛化形式，其中引入了潜在得分以降低潜在异常值的影响，并与常规参数模型协同工作。我们的公式结合了支持集的监督约束以及惩罚查询集上过度自信预测的额外正则项。我们采用块坐标下降法，交替优化潜在得分和参数模型，使二者相互受益。我们将所提出的公式称为《开集似然优化》（OSLO）。OSLO具有可解释性和完全模块化特性，可无缝应用于任何预训练模型之上。通过大量实验表明，我们的方法在开集识别的两个关键方面——内点分类与外点检测——均超越了现有的归纳式和传导式方法。