KGTN-ens: few-shot image classification with knowledge graph ensembles

Dominik Filipiak*, Anna Fensel, Agata Filipowska

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review

Abstract

We propose KGTN-ens, a framework extending the recent Knowledge Graph Transfer Network (KGTN) in order to incorporate multiple knowledge graph embeddings at a small cost. There are many real-world scenarios in which the amount of data is severely limited (e.g. health industry, rare anomalies). Prior knowledge can be used to tackle this task. In KGTN, one can use a single knowledge source at once. The purpose of this study is to investigate the possibility of combining multiple knowledge sources. We evaluate it with different embeddings in a few-shot image classification task. Our model is partially trained on k∈ { 1 , 2 , 5 , 10 } samples. We also construct a new knowledge source – Wikidata embeddings – and evaluate it with KGTN and KGTN-ens. With ResNet50, our approach outperforms KGTN in terms of the top-5 accuracy on the ImageNet-FS dataset for the majority of tested settings. For k∈ { 1 , 2 , 5 , 10 } respectively, we obtained +0.63/+0.58/+0.43/+0.26 pp. (novel classes) and +0.26/+0.25/+0.32/–0.04 pp. (all classes).

Original languageEnglish
Pages (from-to)1893-1908
JournalApplied Intelligence
Volume54
Issue number2
Early online date25 Jan 2024
DOIs
Publication statusPublished - Jan 2024

Keywords

  • Ensemble Learning
  • Few-shot Image Classification
  • Knowledge Graph Enabled AI

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