Hierarchy-guided Neural Networks for Species Classification

O_LIFish species classification is an important task that is the foundation of many industrial, commercial, ecological, and scientific applications involving the study of fish distributions, dynamics, and evolution. C_LIO_LIWhile conventional approaches for this task use off-the-shelf machine learning (ML) methods such as existing Convolutional Neural Network (ConvNet) architectures, there is an opportunity to inform the ConvNet architecture using our knowledge of biological hierarchies among taxonomic classes. C_LIO_LIIn this work, we propose infusing phylogenetic information into the models training to guide its structure and relationships among the extracted features. In our extensive experimental analyses, the proposed model, named Hierarchy-Guided Neural Network (HGNN), outperforms conventional ConvNet models in terms of classification accuracy under scarce training data conditions. C_LIO_LIWe also observe that HGNN shows better resilience to adversarial occlusions, when some of the most informative patch regions of the image are intentionally blocked and their effect on classification accuracy is studied. C_LI