Szczegóły publikacji
Opis bibliograficzny
Image recognition of plants and plant diseases with transfer learning and feature compression / Marcin Ziȩba, Konrad Przewłoka, Michał Grela, Kamil Szkoła, Marcin KUTA // W: Computational Science – ICCS 2023 : 23rd international conference : Prague, Czech Republic, July 3–5, 2023 : proceedings, Pt. 2 / eds. Jiří Mikyška [et al.]. — Cham, Switzerland : Springer, cop. 2023. — (Lecture Notes in Computer Science ; ISSN 0302-9743 ; LNCS 14074). — ISBN: 978-3-031-36020-6; e-ISBN: 978-3-031-36021-3. — S. 204–211. — Bibliogr., Abstr. — Publikacja dostępna online od: 2023-06-26
Autorzy (5)
Słowa kluczowe
Dane bibliometryczne
| ID BaDAP | 147732 |
|---|---|
| Data dodania do BaDAP | 2023-07-20 |
| DOI | 10.1007/978-3-031-36021-3_19 |
| Rok publikacji | 2023 |
| Typ publikacji | materiały konferencyjne (aut.) |
| Otwarty dostęp |  |
| Wydawca | Springer |
| Konferencja | 23rd International Conference on Computational Science |
| Czasopismo/seria | Lecture Notes in Computer Science |
Abstract
This article introduces an easy to implement kick-starting method for transfer learning of image recognition models, meant specifically for training with limited computational resources. The method has two components: (1) Principal Component Analysis transformations of per-filter representations and (2) explicit storage of compressed features. Apart from these two operations, the latent representation of an image is priorly obtained by transforming it via initial layers of the base (donor) model. Taking these measures saves a lot of computations, hence meaningfully speeding up the development. During further work with models, one can directly use the heavily compressed features instead of the original images each time. Despite having a large portion of the donor model frozen, this method yields satisfactory results in terms of prediction accuracy. Such a procedure can be useful for speeding up the early development stages of new models or lowering the potential cost of deployment.