Classifying microfossil radiolarians on fractal pre-trained vision transformers
Abstract While deep learning techniques, especially image classification using deep learning, continue to evolve, it has been noted that there is a large time gap in applying these techniques in geological studies. Recently, a new architecture called the vision transformer (ViT), which is an alterna...
Saved in:
| Main Authors: | Kazuhide Mimura, Takuya Itaki, Hirokatsu Kataoka, Ayumu Miyakawa |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Nature Portfolio
2025-03-01
|
| Series: | Scientific Reports |
| Subjects: | |
| Online Access: | https://doi.org/10.1038/s41598-025-90988-z |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
Review of radiolarian microfossils as a tool for reconstructing sea surface temperature of the past in the Northwest Pacific
by: Kenji M. Matsuzaki, et al.
Published: (2025-05-01) -
Quantifying uncertainty in foraminifera classification: How deep learning methods compare to human experts
by: Iver Martinsen, et al.
Published: (2025-12-01) -
Mimicking early life-forms in the lab
by: Maarten Lubbers, et al.
Published: (2025-08-01) -
Improving computer vision for plant pathology through advanced training techniques
by: Jamie R. Sykes, et al.
Published: (2025-05-01) -
Unravelling neomorphism: recrystallization pathways in Proterozoic microfossiliferous chert
by: Kaitlyn Gauvey, et al.
Published: (2025-05-01)