| 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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