# Tree-CNN: A Hierarchical Deep Convolutional Neural Network for Incremental Learning

arXiv: https://arxiv.org/abs/1802.05800 · HTML: https://mlsanity.com/paper/1802.05800/

## Key facts

- Cited by 34 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 15 Feb 2018.

## Built on by (most cited first, 30 of 34)

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- [RadGraph2: Modeling Disease Progression in Radiology Reports via Hierarchical Information Extraction](https://mlsanity.com/paper/2308.05046/) (2023)
- [Semantic Clustering based Deduction Learning for Image Recognition and Classification](https://mlsanity.com/paper/2112.13165/) (2021)
- [A Continual Learning Framework for Adaptive Defect Classification and Inspection](https://mlsanity.com/paper/2203.08796/) (2022)
- [Online Continual Learning via the Meta-learning Update with Multi-scale Knowledge Distillation and Data Augmentation](https://mlsanity.com/paper/2209.06107/) (2022)
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- [Detecting and Learning the Unknown in Semantic Segmentation](https://mlsanity.com/paper/2202.08700/) (2022)
- [New Insights on Relieving Task-Recency Bias for Online Class Incremental Learning](https://mlsanity.com/paper/2302.08243/) (2023)
- [Encoding Hierarchical Information in Neural Networks helps in Subpopulation Shift](https://mlsanity.com/paper/2112.10844/) (2021)
- [Cold Start Streaming Learning for Deep Networks](https://mlsanity.com/paper/2211.04624/) (2022)
- [AutoEncoding Tree for City Generation and Applications](https://mlsanity.com/paper/2309.15941/) (2023)
- [Improving the Reproducibility of Deep Learning Software: An Initial Investigation through a Case Study Analysis](https://mlsanity.com/paper/2505.03165/) (2025)
- [Efficient Computer Vision on Edge Devices with Pipeline-Parallel Hierarchical Neural Networks](https://mlsanity.com/paper/2109.13356/) (2021)
- [Complex Facial Expression Recognition Using Deep Knowledge Distillation of Basic Features](https://mlsanity.com/paper/2308.06197/) (2023)
- [Low-Power Multi-Camera Object Re-Identification using Hierarchical Neural Networks](https://mlsanity.com/paper/2106.10588/) (2021)
- [Automatic Image Content Extraction: Operationalizing Machine Learning in Humanistic Photographic Studies of Large Visual Archives](https://mlsanity.com/paper/2204.02149/) (2022)
- [Making CNNs Interpretable by Building Dynamic Sequential Decision Forests with Top-down Hierarchy Learning](https://mlsanity.com/paper/2106.02824/) (2021)
- [Effectiveness of the Recent Advances in Capsule Networks](https://mlsanity.com/paper/2210.05834/) (2022)
- [End-to-End Mineral Exploration with Artificial Intelligence and Ambient Noise Tomography](https://mlsanity.com/paper/2403.15095/) (2024)
- [Dual Prototypes for Adaptive Pre-Trained Model in Class-Incremental Learning](https://mlsanity.com/paper/2411.17766/) (2024)
- [Class Incremental Continual Learning with Self-Organizing Maps and Synthetic Replay](https://mlsanity.com/paper/2508.21240/) (2025)
- [hYOLO Model: Enhancing Object Classification with Hierarchical Context in YOLOv8](https://mlsanity.com/paper/2510.23278/) (2025)

## Abstract (arXiv)

Over the past decade, Deep Convolutional Neural Networks (DCNNs) have shown remarkable performance in most computer vision tasks. These tasks traditionally use a fixed dataset, and the model, once trained, is deployed as is. Adding new information to such a model presents a challenge due to complex training issues, such as "catastrophic forgetting", and sensitivity to hyper-parameter tuning. However, in this modern world, data is constantly evolving, and our deep learning models are required to adapt to these changes. In this paper, we propose an adaptive hierarchical network structure composed of DCNNs that can grow and learn as new data becomes available. The network grows in a tree-like fashion to accommodate new classes of data, while preserving the ability to distinguish the previously trained classes. The network organizes the incrementally available data into feature-driven super-classes and improves upon existing hierarchical CNN models by adding the capability of self-growth. The proposed hierarchical model, when compared against fine-tuning a deep network, achieves significant reduction of training effort, while maintaining competitive accuracy on CIFAR-10 and CIFAR-100.