Few-Shot Open-Set Recognition (FSOSR) targets a critical real-world challenge, aiming to categorize inputs into known categories, termed closed-set classes, while identifying open-set inputs that fall outside these classes. Although transfer learning where a model is tuned to a given few-shot task has become a prominent paradigm in closed-world, we observe that it fails to expand to open-world. To unlock this challenge, we propose a two-stage method which consists of open-set aware meta-learning with open-set free transfer learning. In the open-set aware meta-learning stage, a model is trained to establish a metric space that serves as a beneficial starting point for the subsequent stage. During the open-set free transfer learning stage, the model is further adapted to a specific target task through transfer learning. Additionally, we introduce a strategy to simulate open-set examples by modifying the training dataset or generating pseudo open-set examples. The proposed method achieves state-of-the-art performance on two widely recognized benchmarks, miniImageNet and tieredImageNet, with only a 1.5\% increase in training effort. Our work demonstrates the effectiveness of transfer learning in FSOSR.

翻译：少样本开放集识别（FSOSR）针对一个关键的现实世界挑战，旨在将输入分类为已知类别（称为闭集类别），同时识别不属于这些类别的开放集输入。尽管将模型调整到特定少样本任务的迁移学习已成为闭集世界中的主流范式，但我们观察到其无法扩展到开放世界。为攻克这一难题，我们提出了一种两阶段方法，包括开放集感知元学习与开放集无关迁移学习。在开放集感知元学习阶段，模型经过训练以建立一个度量空间，作为后续阶段的有益起点。在开放集无关迁移学习阶段，模型通过迁移学习进一步适应特定目标任务。此外，我们引入了一种通过修改训练数据集或生成伪开放集样本来模拟开放集样本的策略。所提方法在两个广泛认可的基准数据集miniImageNet和tieredImageNet上取得了最先进的性能，且训练成本仅增加1.5%。我们的工作证明了迁移学习在FSOSR中的有效性。