| 研究課題/領域番号 |
22K17960
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| 研究種目 |
若手研究
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| 配分区分 | 基金 |
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| 審査区分 |
小区分61030:知能情報学関連
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| 研究機関 | 国立研究開発法人産業技術総合研究所 |
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研究代表者 |
SALESDESOUZA LINCON 国立研究開発法人産業技術総合研究所, 情報・人間工学領域, 研究員 (40912481)
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| 研究期間 (年度) |
2022-04-01 – 2026-03-31
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| 研究課題ステータス |
交付 (2023年度)
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| 配分額 *注記 |
4,680千円 (直接経費: 3,600千円、間接経費: 1,080千円)
2025年度: 910千円 (直接経費: 700千円、間接経費: 210千円)
2024年度: 1,040千円 (直接経費: 800千円、間接経費: 240千円)
2023年度: 1,820千円 (直接経費: 1,400千円、間接経費: 420千円)
2022年度: 910千円 (直接経費: 700千円、間接経費: 210千円)
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| キーワード | subspace learning / deep neural networks / Subspace learning / Deep neural networks / Manifold optimization / Subspace methods / Pattern recognition |
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| 研究開始時の研究の概要 |
We research a new algorithm for pattern recognition, which are computer programs that allow a machine to automatically recognize regularities in data, such as target objects and events. We mainly focus on the case of recognizing patterns in given multiple images of one object, addressing some inabilities of the current technology called deep learning.
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| 研究実績の概要 |
In year 2023, we continued working on problems of deep learning, attempting to alleviate them by integrating subspace learning aspects to the deep learning framework. We have worked in tasks of action recognition (AR) and domain adaptation (DA); for AR, we devised a new method called slow feature subspace, that improves the capturing of temporal information in videos; and for DA, a new method dubbed domain-sum feature transform, which works efficiently in multi-target domains scenario, a current challenge. We showcase the effectiveness of these methods in their respective tasks through experiments on real image data. We also study their theoretical underpinnings in the Grassmannian geometry, in order to build a strong theoretical foundation for these new methods.
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| 現在までの達成度 (区分) |
現在までの達成度 (区分)
2: おおむね順調に進展している
理由
We have been able to combine subspace learning and deep neural networks to improve the performance in tasks of image set recognition, domain adaptation, action recognition.
We studied the underlying theoretical mechanisms of our newly created techniques/ how they relate to other methods which is useful to expand ourunderstanding of these models.
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| 今後の研究の推進方策 |
We will work on new ways to combine subspace learning and deep neural network that can address their problems and improve performance.
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