Few-Shot Class-Incremental Learning (FSCIL) enables machine learning systems
to expand their inference capabilities to new classes using only a few labeled
examples, without forgetting the previously learned classes. Classical
backpropagation-based learning and its variants are often unsuitable for
battery-powered, memory-constrained systems at the extreme edge. In this work,
we introduce Online Few-Shot Class-Incremental Learning (O-FSCIL), based on a
lightweight model consisting of a pretrained and metalearned feature extractor
and an expandable explicit memory storing the class prototypes. The
architecture is pretrained with a novel feature orthogonality regularization
and metalearned with a multi-margin loss. For learning a new class, our
approach extends the explicit memory with novel class prototypes, while the
remaining architecture is kept frozen. This allows learning previously unseen
classes based on only a few examples with one single pass (hence online).
O-FSCIL obtains an average accuracy of 68.62% on the FSCIL CIFAR100 benchmark,
achieving state-of-the-art results. Tailored for ultra-low-power platforms, we
implement O-FSCIL on the 60 mW GAP9 microcontroller, demonstrating online
learning capabilities within just 12 mJ per new class.

本研究介绍了一种在线少样本增量学习（O-FSCIL）方法，该方法通过使用预训练和元学习的特征提取器以及存储类别原型的可扩展显式内存，使机器学习系统能够仅使用少量标记示例来扩展其推理能力，同时不会忘记先前学习的类别。O-FSCIL 在 FSCIL CIFAR100 基准测试中获得了 68.62％的平均准确率，实现了最先进的结果。针对超低功耗平台，我们将 O-FSCIL 实现在 60 mW 的 GAP9 微控制器上，仅使用 12 mJ 的能量来展示在线学习能力。