Towards out-of-distribution detection for object detection networks
Many studies have recently been published on recognizing when a classification neural network is provided with data that does not fit into one of the class labels learnt during training. These so-called out-of-distribution (OOD) detection approaches have the potential to improve system safety in sit...
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| Format: | Final Year Project (FYP) |
| Language: | English |
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Nanyang Technological University
2022
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| Online Access: | https://hdl.handle.net/10356/157090 |