KGTN-ens: few-shot image classification with knowledge graph ensembles
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).
| Main Authors: | , , |
|---|---|
| Format: | Article/Letter to editor biblioteca |
| Language: | English |
| Subjects: | Ensemble Learning, Few-shot Image Classification, Knowledge Graph Enabled AI, |
| Online Access: | https://research.wur.nl/en/publications/kgtn-ens-few-shot-image-classification-with-knowledge-graph-ensem |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|