dc.contributor.author | Salvesen, Eivind | |
dc.contributor.author | Saad, Aya | |
dc.contributor.author | Stahl, Annette | |
dc.date.accessioned | 2022-10-12T05:54:50Z | |
dc.date.available | 2022-10-12T05:54:50Z | |
dc.date.created | 2021-07-22T10:29:16Z | |
dc.date.issued | 2021 | |
dc.identifier.issn | 0277-786X | |
dc.identifier.uri | https://hdl.handle.net/11250/3025449 | |
dc.description.abstract | Deep convolutional neural networks have proven effective in computer vision, especially in the task of image classification. Nevertheless, the success is limited to supervised learning approaches, requiring extensive amounts of labeled training data that impose time-consuming manual efforts. Unsupervised deep learning methods were introduced to overcome this challenge. The gap, however, towards achieving comparable classification accuracy to supervised learning is still significant. This paper presents a deep learning framework for images of planktonic organisms with no ground truth or manually labeled data. This work combines feature extraction methods using state-of-the-art unsupervised training schemes with clustering algorithms to minimize the labeling effort while improving the classification process based on essential features learned by the deep learning model. The models utilized in the framework are tested over existing planktonic data sets. Empirical results show that unsupervised approaches that cluster the data based on the deep learning model’s feature space representations improve the classification task and can identify classes that have not been seen during the learning process. | en_US |
dc.language.iso | eng | en_US |
dc.publisher | SPIE | en_US |
dc.title | Robust Deep Unsupervised Learning Framework to Discover Unseen Plankton Species | en_US |
dc.type | Journal article | en_US |
dc.type | Peer reviewed | en_US |
dc.description.version | acceptedVersion | en_US |
dc.source.journal | Proceedings of SPIE, the International Society for Optical Engineering | en_US |
dc.identifier.cristin | 1922394 | |
dc.relation.project | Norges forskningsråd: 223254 | en_US |
dc.relation.project | Norges forskningsråd: 262741 | en_US |
dc.description.localcode | Copyright 2021 (year) Society of Photo Optical Instrumentation Engineers (SPIE). One print or electronic copy may be made for personal use only. Systematic reproduction and distribution, duplication of any material in this publication for a fee or for commercial purposes, and modification of the contents of the publication are prohibited. | en_US |
cristin.ispublished | true | |
cristin.fulltext | postprint | |
cristin.qualitycode | 1 | |