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Few-shot learning (FSL) via customization of a deep learning network with
limited data has emerged as a promising technique to achieve personalized user
experiences on edge devices. However, existing FSL methods primarily assume
independent and identically distributed (IID) data and utilize either
computational backpropagation updates for each task or a common model with
task-specific prototypes. Unfortunately, the former solution is infeasible for
edge devices that lack on-device backpropagation capabilities, while the latter
often struggles with limited generalization ability, especially for
out-of-distribution (OOD) data. This paper proposes a lightweight,
plug-and-play FSL module called Task-aware Normalization (TANO) that enables
efficient and task-aware adaptation of a deep neural network without
backpropagation. TANO covers the properties of multiple user groups by
coordinating the updates of several groups of the normalization statistics
during meta-training and automatically identifies the appropriate normalization
group for a downstream few-shot task. Consequently, TANO provides stable but
task-specific estimations of the normalization statistics to close the
distribution gaps and achieve efficient model adaptation. Results on both
intra-domain and out-of-domain generalization experiments demonstrate that TANO
outperforms recent methods in terms of accuracy, inference speed, and model
size. Moreover, TANO achieves promising results on widely-used FSL benchmarks
and data from real applications.