  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.
