Learning from 3D generated synthetic data for unsupervised anomaly detection
Modern machine learning methods, utilising neural networks, require a lot of training data. Data gathering and preparation has thus become a major bottleneck in the machine learning pipeline and researchers often use large public datasets to conduct their research (such as the ImageNet [1] or MNIST...
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| Format: | Others |
| Language: | English |
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Uppsala universitet, Avdelningen för visuell information och interaktion
2021
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| Online Access: | http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-443243 |