Transfer Learning Methods as a New Approach in Computer Vision Tasks with Small Datasets

Brodzicki, Andrzej; Piekarski, Michał; Kucharski, Dariusz; Jaworek-Korjakowska, Joanna; Gorgon, Marek Boguslaw

doi:10.2478/fcds-2020-0010

Cited by 24 publications

(15 citation statements)

References 12 publications

(16 reference statements)

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“…Transfer learning has been recently used to transfer knowledge between different domains. At present, transfer learning has been successfully applied in recognition tasks in computer vision [21,22] and the classification of hyperspectral images [23,24]. As for the application of transfer learning in spectra analysis, Feng et al [15] used transfer learning methods to achieve disease classification for different rice varieties.…”

Section: Introductionmentioning

confidence: 99%

Spectral Preprocessing Combined with Deep Transfer Learning to Evaluate Chlorophyll Content in Cotton Leaves

et al. 2022

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Rapid determination of chlorophyll content is significant for evaluating cotton’s nutritional and physiological status. Hyperspectral technology equipped with multivariate analysis methods has been widely used for chlorophyll content detection. However, the model developed on one batch or variety cannot produce the same effect for another due to variations, such as samples and measurement conditions. Considering that it is costly to establish models for each batch or variety, the feasibility of using spectral preprocessing combined with deep transfer learning for model transfer was explored. Seven different spectral preprocessing methods were discussed, and a self-designed convolutional neural network (CNN) was developed to build models and conduct transfer tasks by fine-tuning. The approach combined first-derivative (FD) and standard normal variate transformation (SNV) was chosen as the best pretreatment. For the dataset of the target domain, fine-tuned CNN based on spectra processed by FD + SNV outperformed conventional partial least squares (PLS) and squares-support vector machine regression (SVR). Although the performance of fine-tuned CNN with a smaller dataset was slightly lower, it was still better than conventional models and achieved satisfactory results. Ensemble preprocessing combined with deep transfer learning could be an effective approach to estimate the chlorophyll content between different cotton varieties, offering a new possibility for evaluating the nutritional status of cotton in the field.

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Section: Introductionmentioning

confidence: 99%

Spectral Preprocessing Combined with Deep Transfer Learning to Evaluate Chlorophyll Content in Cotton Leaves

et al. 2022

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“…In 2019, Jang et al addressed the question of what content to migrate and where to migrate it for transfer learning. As the application of transfer learning continues to expand, it is being used in many areas such as computer vision, − human–computer interaction, text classification, − target recognition, , protein analysis, , and others. Transfer learning has proven to be a powerful technique in biology and chemistry, with applications in gene expression data analysis , and neuroscience research .…”

Section: Methods For Small Molecular Data Challengesmentioning

confidence: 99%

Machine Learning Methods for Small Data Challenges in Molecular Science

et al. 2023

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Small data are often used in scientific and engineering research due to the presence of various constraints, such as time, cost, ethics, privacy, security, and technical limitations in data acquisition. However, big data have been the focus for the past decade, small data and their challenges have received little attention, even though they are technically more severe in machine learning (ML) and deep learning (DL) studies. Overall, the small data challenge is often compounded by issues, such as data diversity, imputation, noise, imbalance, and high-dimensionality. Fortunately, the current big data era is characterized by technological breakthroughs in ML, DL, and artificial intelligence (AI), which enable data-driven scientific discovery, and many advanced ML and DL technologies developed for big data have inadvertently provided solutions for small data problems. As a result, significant progress has been made in ML and DL for small data challenges in the past decade. In this review, we summarize and analyze several emerging potential solutions to small data challenges in molecular science, including chemical and biological sciences. We review both basic machine learning algorithms, such as linear regression, logistic regression (LR), k-nearest neighbor (KNN), support vector machine (SVM), kernel learning (KL), random forest (RF), and gradient boosting trees (GBT), and more advanced techniques, including artificial neural network (ANN), convolutional neural network (CNN), U-Net, graph neural network (GNN), Generative Adversarial Network (GAN), long short-term memory (LSTM), autoencoder, transformer, transfer learning, active learning, graph-based semi-supervised learning, combining deep learning with traditional machine learning, and physical model-based data augmentation. We also briefly discuss the latest advances in these methods. Finally, we conclude the survey with a discussion of promising trends in small data challenges in molecular science.

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“…DCNNs contain massive parameters, and they should be trained on large datasets such as ImageNet (https://image-net.org; Deng et al 2009), which contains more than 1.2 million labeled images from 1000 classes. Alternatively, transfer learning can be used for small training datasets (Tan et al 2018;Brodzicki et al 2020;Koeshidayatullah et al 2020). In transfer learning, instead of training a CNN architecture from randomly initialized parameters, the parameters are obtained from pretraining on other recognition tasks with a large dataset for initialization.…”

Section: Methodsmentioning

confidence: 99%

Automatic taxonomic identification based on the Fossil Image Dataset (>415,000 images) and deep convolutional neural networks

Liu¹,

Jiang²,

Wu³

et al. 2022

Paleobiology

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The rapid and accurate taxonomic identification of fossils is of great significance in paleontology, biostratigraphy, and other fields. However, taxonomic identification is often labor-intensive and tedious, and the requisition of extensive prior knowledge about a taxonomic group also requires long-term training. Moreover, identification results are often inconsistent across researchers and communities. Accordingly, in this study, we used deep learning to support taxonomic identification. We used web crawlers to collect the Fossil Image Dataset (FID) via the Internet, obtaining 415,339 images belonging to 50 fossil clades. Then we trained three powerful convolutional neural networks on a high-performance workstation. The Inception-ResNet-v2 architecture achieved an average accuracy of 0.90 in the test dataset when transfer learning was applied. The clades of microfossils and vertebrate fossils exhibited the highest identification accuracies of 0.95 and 0.90, respectively. In contrast, clades of sponges, bryozoans, and trace fossils with various morphologies or with few samples in the dataset exhibited a performance below 0.80. Visual explanation methods further highlighted the discrepancies among different fossil clades and suggested similarities between the identifications made by machine classifiers and taxonomists. Collecting large paleontological datasets from various sources, such as the literature, digitization of dark data, citizen-science data, and public data from the Internet may further enhance deep learning methods and their adoption. Such developments will also possibly lead to image-based systematic taxonomy to be replaced by machine-aided classification in the future. Pioneering studies can include microfossils and some invertebrate fossils. To contribute to this development, we deployed our model on a server for public access at www.ai-fossil.com.

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Transfer Learning Methods as a New Approach in Computer Vision Tasks with Small Datasets

Cited by 24 publications

References 12 publications

Spectral Preprocessing Combined with Deep Transfer Learning to Evaluate Chlorophyll Content in Cotton Leaves

Spectral Preprocessing Combined with Deep Transfer Learning to Evaluate Chlorophyll Content in Cotton Leaves

Machine Learning Methods for Small Data Challenges in Molecular Science

Automatic taxonomic identification based on the Fossil Image Dataset (>415,000 images) and deep convolutional neural networks

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