# Learn Class Hierarchy using Convolutional Neural Networks

arXiv: https://arxiv.org/abs/2005.08622 · HTML: https://mlsanity.com/paper/2005.08622/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 18 May 2020.

## Built on by (most cited first, 7 of 7)

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- [EnGraf-Net: Multiple Granularity Branch Network with Fine-Coarse Graft Grained for Classification Task](https://mlsanity.com/paper/2509.21061/) (2025)
- [hYOLO Model: Enhancing Object Classification with Hierarchical Context in YOLOv8](https://mlsanity.com/paper/2510.23278/) (2025)

## Abstract (arXiv)

A large amount of research on Convolutional Neural Networks has focused on flat Classification in the multi-class domain. In the real world, many problems are naturally expressed as problems of hierarchical classification, in which the classes to be predicted are organized in a hierarchy of classes. In this paper, we propose a new architecture for hierarchical classification of images, introducing a stack of deep linear layers with cross-entropy loss functions and center loss combined. The proposed architecture can extend any neural network model and simultaneously optimizes loss functions to discover local hierarchical class relationships and a loss function to discover global information from the whole class hierarchy while penalizing class hierarchy violations. We experimentally show that our hierarchical classifier presents advantages to the traditional classification approaches finding application in computer vision tasks.