  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
