Few-shot learning has made impressive strides in addressing the crucial challenges of recognizing unknown samples from novel classes in target query sets and managing visual shifts between domains. However, existing techniques fall short when it comes to identifying target outliers under domain shifts by learning to reject pseudo-outliers from the source domain, resulting in an incomplete solution to both problems. To address these challenges comprehensively, we propose a novel approach called Domain Adaptive Few-Shot Open Set Recognition (DA-FSOS) and introduce a meta-learning-based architecture named DAFOSNET. During training, our model learns a shared and discriminative embedding space while creating a pseudo open-space decision boundary, given a fully-supervised source domain and a label-disjoint few-shot target domain. To enhance data density, we use a pair of conditional adversarial networks with tunable noise variances to augment both domains closed and pseudo-open spaces. Furthermore, we propose a domain-specific batch-normalized class prototypes alignment strategy to align both domains globally while ensuring class-discriminativeness through novel metric objectives. Our training approach ensures that DAFOS-NET can generalize well to new scenarios in the target domain. We present three benchmarks for DA-FSOS based on the Office-Home, mini-ImageNet/CUB, and DomainNet datasets and demonstrate the efficacy of DAFOS-NET through extensive experimentation

翻译：小样本学习在应对从新类目标查询集中识别未知样本以及管理领域间视觉偏移等关键挑战方面取得了显著进展。然而，现有技术在通过从源领域学习拒绝伪异常样本来识别领域偏移下的目标异常值方面存在不足，导致对这两个问题的解决方案不完整。为全面应对这些挑战，我们提出了一种名为领域自适应小样本开集识别（DA-FSOS）的新方法，并引入了一种基于元学习的架构DAFOS-NET。在训练过程中，我们的模型在给定完全监督的源领域和标签不交叠的小样本目标领域时，学习一个共享且具有判别性的嵌入空间，同时创建一个伪开放空间决策边界。为增强数据密度，我们使用一对具有可调噪声方差的条件对抗网络来扩充两个领域的封闭空间和伪开放空间。此外，我们提出了一种领域特定的批量归一化类原型对齐策略，以实现两个领域的全局对齐，同时通过新颖的度量目标确保类别判别性。我们的训练方法确保DAFOS-NET能够很好地泛化到目标领域的新场景。我们基于Office-Home、mini-ImageNet/CUB和DomainNet数据集构建了三个DA-FSOS基准，并通过大量实验证明了DAFOS-NET的有效性。