基于高光谱图像的蜂蜜植物来源分类的类别增量学习：持续反向传播研究
Class-Incremental Learning for Honey Botanical Origin Classification with Hyperspectral Images: A Study with Continual Backpropagation
AI总结：
针对蜂蜜植物来源分类难以一次性收集全品类数据训练模型的问题，本研究在真实蜂蜜高光谱数据集上对比多种类别增量学习算法，提出结合持续反向传播的方法，通过重激活低使用频率隐藏神经元缓解可塑性丧失，将多数算法性能提升1-7%。
摘要：
蜂蜜是全球市场上的重要商品。不同植物来源的蜂蜜品类风味多样、健康功效各异，因此市场价值也存在差异。开发精准有效的植物来源鉴别技术对保护消费者权益至关重要。然而，一次性收集所有蜂蜜产品品种来训练植物来源区分模型并不现实，因此研究人员开发了类别增量学习（Class-Incremental Learning, CIL）技术来应对这一挑战。本研究在真实世界的蜂蜜高光谱成像数据集上检验并比较了多种CIL算法，还提出了一种结合持续反向传播（Continual Backpropagation, CB）算法的新技术，以提升类别增量学习算法的性能。CB方法通过重新初始化一定比例使用频率较低的隐藏神经元，为神经网络注入可变性，从而解决可塑性丧失问题。实验表明，CB将大多数CIL方法的性能提升了1-7%。
英文摘要：
Honey is an important commodity in the global market. Honey types of different botanical origins provide diversified flavors and health benefits, thus having different market values. Developing accurate and effective botanical origin-distinguishing techniques is crucial to protect consumers' interests. However, it is impractical to collect all the varieties of honey products at once to train a model for botanical origin differentiation. Therefore, researchers developed class-incremental learning (CIL) techniques to address this challenge. This study examined and compared multiple CIL algorithms on a real-world honey hyperspectral imaging dataset. A novel technique is also proposed to improve the performance of class-incremental learning algorithms by combining with a continual backpropagation (CB) algorithm. The CB method addresses the issue of loss-of-plasticity by reinitializing a proportion of less-used hidden neurons to inject variability into neural networks. Experiments showed that CB improved the performance of most CIL methods by 1-7\%.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
