Training Multi-Task Deep Neural Networks with Disjoint Datasets
This work examines training neural networks which are capable of learning multiple tasks. We propose an architecture trained on KITTI and Cityscapes, which respectively include only the annotations for 2D object detection and semantic segmentation. We propose 4 methods for training with disjoint dat...
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| Format: | Others |
| Language: | English |
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KTH, Skolan för elektroteknik och datavetenskap (EECS)
2020
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| Online Access: | http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-283652 |