In response to the prevalent challenge of overfitting in deep neural networks, this paper introduces Simultaneous Learning, a regularization approach drawing on principles of Transfer Learning and Multi-task Learning. We leverage auxiliary datasets with the target dataset, the UFOP-HVD, to facilitate simultaneous classification guided by a customized loss function featuring an inter-group penalty. This experimental configuration allows for a detailed examination of model performance across similar (PlantNet) and dissimilar (ImageNet) domains, thereby enriching the generalizability of Convolutional Neural Network models. Remarkably, our approach demonstrates superior performance over models without regularization and those applying dropout regularization exclusively, enhancing accuracy by 5 to 22 percentage points. Moreover, when combined with dropout, the proposed approach improves generalization, securing state-of-the-art results for the UFOP-HVD challenge. The method also showcases efficiency with significantly smaller sample sizes, suggesting its broad applicability across a spectrum of related tasks. In addition, an interpretability approach is deployed to evaluate feature quality by analyzing class feature correlations within the network's convolutional layers. The findings of this study provide deeper insights into the efficacy of Simultaneous Learning, particularly concerning its interaction with the auxiliary and target datasets.

翻译：针对深度神经网络中普遍存在的过拟合问题，本文提出了一种名为"联合学习"的正则化方法，该方法借鉴了迁移学习和多任务学习的原理。我们利用辅助数据集与目标数据集UFOP-HVD，通过引入带有组间惩罚的自定义损失函数，实现联合分类。这一实验配置能够详细考察模型在相似领域（PlantNet）与非相似领域（ImageNet）上的性能表现，从而增强卷积神经网络模型的泛化能力。值得注意的是，我们的方法在性能上优于未采用正则化的模型以及仅应用丢弃正则化的模型，准确率提升5至22个百分点。此外，当与丢弃法结合使用时，该方法进一步改善了泛化能力，在UFOP-HVD挑战中取得了最先进的结果。该方法在小样本条件下同样展现出高效性，表明其在广泛的相关任务中具有普适性。同时，我们采用了一种可解释性方法，通过分析网络卷积层中的类别特征相关性来评估特征质量。本研究的发现为联合学习的有效性提供了更深入的见解，特别是其与辅助数据集和目标数据集的交互机制。