Lightweight Few-Shot Learning Based on Triple Information for Internet-of-Things Applications

Under the stringent requirements of latency, reliability and privacy in IoT scenarios, IoT intelligence has gradually sunk to endpoint devices. However, in this process of deploying the deep learning models on endpoint devices, the challenges of constrained data resources and computing resources are encountered. In order to address these challenges, we propose in this paper a lightweight few-shot learning algorithm by the use of limited data and small-scale network structure, called TI-FSL (triple information based few-shot learning), which not only has strong generalization ability but also can be deployed on the resource-constrained endpoint devices for IoT applications. Our algorithm proceeds in 3 steps. First, we construct triples for few-shot classification, in which the anchor is from the query set and the positive and the negative belong to the support set. Second, based on the above triples, the triple information is measured by the maximum triplet loss of the anchor, positive class and different negative classes. Third, the lightweight classifier can be learned by optimizing the triple information to update the network structure. We evaluate our algorithm on the most popular few-shot dataset
mini
ImageNet and an IoT activity recognition dataset
SDA
; and the results demonstrate the superiority of our algorithm against other state-of-the-art methods.