2023 Fiscal Year Research-status Report
Development of learning subspace-based methods for pattern recognition
| Project/Area Number |
22K17960
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| Research Institution | National Institute of Advanced Industrial Science and Technology |
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Principal Investigator |
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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| Project Period (FY) |
2022-04-01 – 2026-03-31
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| Keywords | subspace learning / deep neural networks |
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| Outline of Annual Research Achievements |
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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| Current Status of Research Progress |
Current Status of Research Progress
2: Research has progressed on the whole more than it was originally planned.
Reason
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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| Strategy for Future Research Activity |
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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| Causes of Carryover |
To cover cloud computing costs to use in the next fiscal year and to attend conferences.
Plan: We plan to purchase cloud computing that can process large-scale data at high speed and attend conferences to gather necessary information
on the latest technologies and/or present our research.
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