Few-shot vision recognition and generation for the open-world
Deep Neural Networks (DNNs) have achieved remarkable success across various computer vision tasks, but their reliance on extensive labeled datasets limits their applicability in data-scarce scenarios. Few-shot learning offers a promising solution by enabling models to learn from minimal data, yet tr...
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格式: | Thesis-Doctor of Philosophy |
語言: | English |
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Nanyang Technological University
2024
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在線閱讀: | https://hdl.handle.net/10356/181293 |
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機構: | Nanyang Technological University |
語言: | English |