The experimental process of our method (FM-DLM).

Currently, deep learning models are widely used in many classification applications, but their utilization is limited by some factors. The large models can ensure classification of wide range, but they cannot be deployed to some small devices. The small models can be deployed to the small devices, but the number of labels is limited. To solve these problems, this paper proposes a classification method based on the Fusion of Multi-level Deep Learning Models (FM-DLM). We apply the Baidu-AI platform as a Level 0 model for classification of wide range samples. Then, we use the difference between Level 1 models to perform dataset prediction. Then, we can use the Level 2 models that were trained on the predicted dataset, which is to perform label classification. Finally, we use label distribution to achieve higher accuracy. The experimental results show that our method can achieve higher accuracy than the existing methods while ensuring a wide range of classification.

当前，深度学习模型（deep learning models）已在诸多分类应用场景中得到广泛应用，但其实际部署与应用仍受多项因素制约。大模型可实现大范围的分类覆盖，但无法部署至部分小型设备；小型模型虽可部署于小型设备，但其可支持的标签数量有限。为解决上述问题，本文提出一种基于多级深度学习模型融合（Fusion of Multi-level Deep Learning Models, FM-DLM）的分类方法。我们将百度AI（Baidu-AI）平台作为0级（Level 0）模型，用于大范围样本的分类任务；随后借助1级（Level 1）模型间的预测差异完成数据集预测；再依托在该预测数据集上训练得到的2级（Level 2）模型，完成标签级分类；最终通过整合标签分布信息，进一步提升分类精度。实验结果表明，所提方法在保障大范围分类覆盖的同时，可实现优于现有方法的分类精度。