Towards robust visual recognition: learning from imperfect data
Though Deep Convolutional Neural Networks (DCNN) have shown success in many tasks in the field of computer vision, the huge effort made in constructing large-scale annotated datasets is indispensable. Even the prevailing models can fail when the dataset does not cover enough samples. For example, fo...
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| 格式: | Thesis-Doctor of Philosophy |
| 語言: | English |
| 出版: |
Nanyang Technological University
2023
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| 主題: | |
| 在線閱讀: | https://hdl.handle.net/10356/164415 |
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