Do more with less: Exploring semi-supervised learning for geological image classification

IF 2.6 Q2 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS Applied Computing and Geosciences Pub Date : 2025-02-01 DOI:10.1016/j.acags.2024.100216
Hisham I. Mamode, Gary J. Hampson, Cédric M. John
{"title":"Do more with less: Exploring semi-supervised learning for geological image classification","authors":"Hisham I. Mamode,&nbsp;Gary J. Hampson,&nbsp;Cédric M. John","doi":"10.1016/j.acags.2024.100216","DOIUrl":null,"url":null,"abstract":"<div><div>Labelled datasets within geoscience can often be small, with data acquisition both costly and challenging, and their interpretation and downstream use in machine learning difficult due to data scarcity. Deep learning algorithms require large datasets to learn a robust relationship between the data and its label and avoid overfitting. To overcome the paucity of data, transfer learning has been employed in classification tasks. But an alternative exists: there often is a large corpus of unlabeled data which may enhance the learning process. To evaluate this potential for subsurface data, we compare a high-performance semi-supervised learning (SSL) algorithm (SimCLRv2) with supervised transfer learning on a Convolutional Neural Network (CNN) in geological image classification.</div><div>We tested the two approaches on a classification task of sediment disturbance from cores of International Ocean Drilling Program (IODP) Expeditions 383 and 385. Our results show that semi-supervised transfer learning can be an effective strategy to adopt, with SimCLRv2 capable of producing representations comparable to those of supervised transfer learning. However attempts to enhance the performance of semi-supervised transfer learning with task-specific unlabeled images during self-supervision degraded representations. Significantly, we demonstrate that SimCLRv2 trained on a dataset of core disturbance images can out-perform supervised transfer learning of a CNN once a critical number of task-specific unlabeled images are available for self-supervision. The gain in performance compared to supervised transfer learning is 1% and 3% for binary and multi-class classification, respectively.</div><div>Supervised transfer learning can be deployed with comparative ease, whereas the current SSL algorithms such as SimCLRv2 require more effort. We recommend that SSL be explored in cases when large amounts of unlabeled task-specific images exist and improvement of a few percent in metrics matter. When examining small, highly specialized datasets, without large amounts of unlabeled images, supervised transfer learning might be the best strategy to adopt. Overall, SSL is a promising approach and future work should explore this approach utilizing different dataset types, quantity, and quality.</div></div>","PeriodicalId":33804,"journal":{"name":"Applied Computing and Geosciences","volume":"25 ","pages":"Article 100216"},"PeriodicalIF":2.6000,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Applied Computing and Geosciences","FirstCategoryId":"1085","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S2590197424000636","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS","Score":null,"Total":0}
引用次数: 0

Abstract

Labelled datasets within geoscience can often be small, with data acquisition both costly and challenging, and their interpretation and downstream use in machine learning difficult due to data scarcity. Deep learning algorithms require large datasets to learn a robust relationship between the data and its label and avoid overfitting. To overcome the paucity of data, transfer learning has been employed in classification tasks. But an alternative exists: there often is a large corpus of unlabeled data which may enhance the learning process. To evaluate this potential for subsurface data, we compare a high-performance semi-supervised learning (SSL) algorithm (SimCLRv2) with supervised transfer learning on a Convolutional Neural Network (CNN) in geological image classification.
We tested the two approaches on a classification task of sediment disturbance from cores of International Ocean Drilling Program (IODP) Expeditions 383 and 385. Our results show that semi-supervised transfer learning can be an effective strategy to adopt, with SimCLRv2 capable of producing representations comparable to those of supervised transfer learning. However attempts to enhance the performance of semi-supervised transfer learning with task-specific unlabeled images during self-supervision degraded representations. Significantly, we demonstrate that SimCLRv2 trained on a dataset of core disturbance images can out-perform supervised transfer learning of a CNN once a critical number of task-specific unlabeled images are available for self-supervision. The gain in performance compared to supervised transfer learning is 1% and 3% for binary and multi-class classification, respectively.
Supervised transfer learning can be deployed with comparative ease, whereas the current SSL algorithms such as SimCLRv2 require more effort. We recommend that SSL be explored in cases when large amounts of unlabeled task-specific images exist and improvement of a few percent in metrics matter. When examining small, highly specialized datasets, without large amounts of unlabeled images, supervised transfer learning might be the best strategy to adopt. Overall, SSL is a promising approach and future work should explore this approach utilizing different dataset types, quantity, and quality.
查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
求助全文
约1分钟内获得全文 去求助
来源期刊
Applied Computing and Geosciences
Applied Computing and Geosciences Computer Science-General Computer Science
CiteScore
5.50
自引率
0.00%
发文量
23
审稿时长
5 weeks
期刊最新文献
Deformation analysis by an improved similarity transformation Irrigated rice-field mapping in Brazil using phenological stage information and optical and microwave remote sensing Pymaginverse: A python package for global geomagnetic field modeling Automatic variogram inference using pre-trained Convolutional Neural Networks X-ray Micro-CT based characterization of rock cuttings with deep learning
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1