Label Hierarchy Transition: Delving into Class Hierarchies to Enhance Deep Classifiers

2021-12-04 · Source: cs.CV updates on arXiv.org · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Data Science & Analytics · Depth: Expert, quick

Summary

The Label Hierarchy Transition (LHT) framework is a unified probabilistic deep learning approach designed to enhance deep classifiers in hierarchical classification tasks. Unlike existing multi-task learning strategies that often fail to fully exploit inter-level correlations, LHT explicitly learns these relationships. The framework comprises a transition network, which encodes underlying correlations within class hierarchies by learning label hierarchy transition matrices, and a confusion loss, which encourages the classification network to learn correlations across different label hierarchies during training. LHT can be readily adapted to any existing deep network with minor modifications. Experimental results on public benchmark datasets demonstrate its superiority over current methods, and its potential is validated through an extension to skin lesion diagnosis.

Key takeaway

For Machine Learning Engineers developing hierarchical classification systems, the Label Hierarchy Transition (LHT) framework offers a robust method to improve model performance. You should consider integrating LHT's transition network and confusion loss into your existing deep learning architectures to better exploit inter-level category correlations, potentially achieving superior results on complex classification tasks like medical diagnosis.

Key insights

LHT enhances hierarchical classification by explicitly learning label hierarchy transitions and cross-level correlations.

Principles

• Existing multi-task learning often underutilizes inter-level category correlations.
• Explicitly learning transition matrices encodes underlying class hierarchy correlations.
• A confusion loss can foster correlation learning across different label hierarchies.

Method

LHT employs a transition network to learn label hierarchy transition matrices and a confusion loss to encourage cross-hierarchy correlation learning, integrating into existing deep networks with minor modifications.

In practice

• Improve deep classifiers for hierarchical classification problems.
• Apply to computer-aided diagnosis, such as skin lesion diagnosis.
• Integrate into any existing deep network with minimal changes.

Topics

• Hierarchical Classification
• Deep Learning
• Label Hierarchy Transition
• Transition Networks
• Confusion Loss
• Computer-Aided Diagnosis

Code references

• renzhenwang/label-hierarchy-transition

Best for: Computer Vision Engineer, AI Scientist, Machine Learning Engineer, Research Scientist

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Editorial summary, takeaway, and curation by AIssential. Original article published by cs.CV updates on arXiv.org.