深度神经网络是一种需要大量的数据来进行有效训练的模型。军事装备类数据普遍存在数据量较少,无法满足深度神经网络的训练需求,容易出现过拟合的问题。针对该问题,本文引入迁移学习技术,通过构建多类型样本训练集,微调预训练模型,构建军事装备类集成分类器。实践证明迁移学习在少样本分类任务中的应用节省了模型训练时间,解决了模型过拟合及对数据标签依赖性强的问题,能有效提高基于深度学习的军事装备类小样本图像分类的准确性。A large amount of data is indispensable for effective training of deep neural networks. Military equipment data generally suffers from insufficient quantities, which fails to meet the training requirements of deep neural networks and easily leads to over fitting. To address this issue, this paper introduces transfer learning technology by constructing a multi-type sample training set and fine-tuning pre-trained models, and an ensemble classifier for military equipment is built. Experimental results have confirmed that transfer learning saves training time on small samples tasks, resolves issues of model over fitting and strong dependence on data labels simultaneously, and can effectively improve the accuracy of small sample image classification of military equipment based on deep learning.
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