Dataset Information

Classification of masked image data.

ABSTRACT: Data classification is one of the most commonly used applications of machine learning. The are many developed algorithms that can work in various environments and for different data distributions that perform this task with excellence. Classification algorithms, just like other machine learning algorithms have one thing in common: in order to operate on data, they must see the data. In the present world, where concerns about privacy, GDPR (General Data Protection Regulation), business confidentiality and security are growing bigger and bigger; this requirement to work directly on the original data might become, in some situations, a burden. In this paper, an approach to the classification of images that cannot be directly accessed during training has been made. It has been shown that one can train a deep neural network to create such a representation of the original data that i) without additional information, the original data cannot be restored, and ii) that this representation-called a masked form-can still be used for classification purposes. Moreover, it has been shown that classification of the masked data can be done using both classical and neural network-based classifiers.

SUBMITTER: Lis K

PROVIDER: S-EPMC8259988 | biostudies-literature | 2021

REPOSITORIES: biostudies-literature

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Publications

Classification of masked image data.

Lis Kamila K Koryciński Mateusz M Ciecierski Konrad A KA

PloS one 20210706 7

Data classification is one of the most commonly used applications of machine learning. The are many developed algorithms that can work in various environments and for different data distributions that perform this task with excellence. Classification algorithms, just like other machine learning algorithms have one thing in common: in order to operate on data, they must see the data. In the present world, where concerns about privacy, GDPR (General Data Protection Regulation), business confidenti ...[more]

PMID: 34228756

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Project description:The classification of remote sensing images is inherently challenging due to the complexity, diversity, and sparsity of the data across different image samples. Existing advanced methods often require substantial modifications to model architectures to achieve optimal performance, resulting in complex frameworks that are difficult to adapt. To overcome these limitations, we propose a lightweight ensemble method, enhanced by pure data correction, called the Exceptionally Straightforward Ensemble. This approach eliminates the need for extensive structural modifications to models. A key innovation in our method is the introduction of a novel strategy, quantitative augmentation, implemented through a plug-and-play module. This strategy effectively corrects feature distributions across remote sensing data, significantly improving the performance of Convolutional Neural Networks and Vision Transformers beyond traditional data augmentation techniques. Furthermore, we propose a straightforward algorithm to generate an ensemble network composed of two components, serving as the proposed lightweight classifier. We evaluate our method on three well-known datasets, with results demonstrating that our ensemble models outperform 48 state-of-the-art methods published since 2020, excelling in accuracy, inference speed, and model compactness. Specifically, our models achieve an overall accuracy of up to 96.8%, representing a 1.1% improvement on the challenging NWPU45 dataset. Moreover, the smallest model in our ensemble reduces parameters by up to 90% and inference time by 74%. Notably, our approach significantly enhances the performance of Convolutional Neural Networks and Vision Transformers, even with limited training data, thus alleviating the performance dependence on large-scale datasets. In summary, our data-driven approach offers an efficient, accessible solution for remote sensing image classification, providing an elegant alternative for researchers in geoscience fields who may have limited time or resources for model optimization.

Dataset Information

Classification of masked image data.

Publications

Classification of masked image data.

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