In the realm of practical fine-grained visual classification applications rooted in deep learning, a common scenario involves training a model using a pre-existing dataset. Subsequently, a new dataset becomes available, prompting the desire to make a pivotal decision for achieving enhanced and leveraged inference performance on both sides: Should one opt to train datasets from scratch or fine-tune the model trained on the initial dataset using the newly released dataset? The existing literature reveals a lack of methods to systematically determine the optimal training strategy, necessitating explainability. To this end, we present an automatic best-suit training solution searching framework, the Dual-Carriageway Framework (DCF), to fill this gap. DCF benefits from the design of a dual-direction search (starting from the pre-existing or the newly released dataset) where five different training settings are enforced. In addition, DCF is not only capable of figuring out the optimal training strategy with the capability of avoiding overfitting but also yields built-in quantitative and visual explanations derived from the actual input and weights of the trained model. We validated DCF's effectiveness through experiments with three convolutional neural networks (ResNet18, ResNet34 and Inception-v3) on two temporally continued commercial product datasets. Results showed fine-tuning pathways outperformed training-from-scratch ones by up to 2.13% and 1.23% on the pre-existing and new datasets, respectively, in terms of mean accuracy. Furthermore, DCF identified reflection padding as the superior padding method, enhancing testing accuracy by 3.72% on average. This framework stands out for its potential to guide the development of robust and explainable AI solutions in fine-grained visual classification tasks.

翻译：在基于深度学习的实际细粒度视觉分类应用中，常见场景是利用现有数据集训练模型后，新数据集出现时需做出关键决策以提升双方推理性能：是应从头训练数据集，还是利用新数据集对初始模型进行微调？现有文献缺乏系统性确定最优训练策略的方法，亟需可解释性。为此，我们提出一种自动最佳训练方案搜索框架——双通道框架（DCF），以填补这一空白。DCF得益于双向搜索设计（从现有数据集或新数据集出发），强制执行五种不同训练设置。此外，DCF不仅能够找出最优训练策略并避免过拟合，还能基于训练模型的实际输入和权重生成内置的定量与可视化解释。我们通过三个卷积神经网络（ResNet18、ResNet34和Inception-v3）在两组时间连续的商业产品数据集上实验验证了DCF的有效性。结果表明，在平均准确率方面，微调路径在现有数据集和新数据集上分别比从头训练路径高出最多2.13%和1.23%。此外，DCF识别出反射填充为更优填充方法，使测试准确率平均提升3.72%。该框架在细粒度视觉分类任务中具有指导开发鲁棒可解释人工智能解决方案的显著潜力。