Adversarial active learning with feature mixing

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ID: 286010
2024
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Abstract
In the field of computer vision, training accurate deep learning models necessitates a substantial number of manually annotated images, often posing financial and practical challenges, particularly in domains like medicine and engineering. This research builds upon existing literature in computer vision and active learning, identifying gaps in current approaches and proposing an innovative strategy that combines diversity-based active learning sampling with feature mixing. This novel approach aims to select unlabeled samples that exhibit both diversity and informativeness, thereby enhancing model performance while managing annotation budgets effectively. However, this thesis recognizes the inherent challenges in classifying high-dimensional data with limited samples. By bridging the gap between diversity-based active learning and feature mixing, this research contributes to advancing the state of the art in computer vision and holds the potential to impact not only the academic community but also real- world applications in medical imaging, engineering, and related fields. It addresses the pressing need for more efficient and cost-effective deep learning model training in domains where expert annotation is crucial.
Reference Key
persistent_1760657307_68f17f9bad12a Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Tiberio, Janos Lance L.
Journal Malay Journal
Year 2024
DOI
DOI not found
URL
https://animorepository.dlsu.edu.ph/etdm_ece/33
Keywords Keywords not found

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