# Improving Detection of Rare Nodes in Hierarchical Multi-Label Learning

arXiv: https://arxiv.org/abs/2602.08986 · HTML: https://mlsanity.com/paper/2602.08986/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 17 of its 42 references that are in the map.
- First posted on arXiv 9 Feb 2026.

## Builds on (most cited first, 17 of 17)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [What Uncertainties Do We Need in Bayesian Deep Learning for Computer Vision?](https://mlsanity.com/paper/1703.04977/) (2017)
- [Natural Adversarial Examples](https://mlsanity.com/paper/1907.07174/) (2019)
- [The Open Images Dataset V4: Unified image classification, object detection, and visual relationship detection at scale](https://mlsanity.com/paper/1811.00982/) (2018)
- [Why M Heads are Better than One: Training a Diverse Ensemble of Deep Networks](https://mlsanity.com/paper/1511.06314/) (2015)
- [Quantifying Aleatoric and Epistemic Uncertainty in Machine Learning: Are Conditional Entropy and Mutual Information Appropriate Measures?](https://mlsanity.com/paper/2209.03302/) (2022)
- [How Deep Learning Sees the World: A Survey on Adversarial Attacks & Defenses](https://mlsanity.com/paper/2305.10862/) (2023)
- [Coherent Hierarchical Multi-Label Classification Networks](https://mlsanity.com/paper/2010.10151/) (2020)
- [Introducing an Improved Information-Theoretic Measure of Predictive Uncertainty](https://mlsanity.com/paper/2311.08309/) (2023)
- [FathomNet: A global image database for enabling artificial intelligence in the ocean](https://mlsanity.com/paper/2109.14646/) (2021)
- [Understanding Model Calibration -- A gentle introduction and visual exploration of calibration and the expected calibration error (ECE)](https://mlsanity.com/paper/2501.19047/) (2025)
- [Multi-Label Sampling based on Local Label Imbalance](https://mlsanity.com/paper/2005.03240/) (2020)
- [BenthicNet: A global compilation of seafloor images for deep learning applications](https://mlsanity.com/paper/2405.05241/) (2024)
- [Uncertainty Estimation using Variance-Gated Distributions](https://mlsanity.com/paper/2509.08846/) (2025)
- [Hierarchy exploitation to detect missing annotations on hierarchical multi-label classification](https://mlsanity.com/paper/2207.06237/) (2022)
- [Hierarchical Classification for Automated Image Annotation of Coral Reef Benthic Structures](https://mlsanity.com/paper/2412.08228/) (2024)
- [ReefNet: A Large-Scale Dataset and Benchmark for Fine-Grained Coral Reef Recognition](https://mlsanity.com/paper/2510.16822/) (2025)

## Abstract (arXiv)

In hierarchical multi-label classification, a persistent challenge is enabling model predictions to reach deeper levels of the hierarchy for more detailed or fine-grained classifications. This difficulty partly arises from the natural rarity of certain classes (or hierarchical nodes) and the hierarchical constraint that ensures child nodes are almost always less frequent than their parents. To address this, we propose a weighted loss objective for neural networks that combines node-wise imbalance weighting with focal weighting components, the latter leveraging modern quantification of ensemble uncertainties. By emphasizing rare nodes rather than rare observations (data points), and focusing on uncertain nodes for each model output distribution during training, we observe improvements in recall by up to a factor of five on benchmark datasets, along with statistically significant gains in F1 score. We also show our approach aids convolutional networks on challenging tasks, as in situations with suboptimal encoders or limited data.