Combining simulated and real images in deep learning
To train a deep learning (DL) model, considerable amounts of data are required to generalize to unseen cases successfully. Furthermore, such data is often manually labeled, making its annotation process costly and time-consuming. We propose the use of simulated data, obtained from simulators, as a w...
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| Formato: | masterThesis |
| Idioma: | eng |
| Publicado em: |
2021
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| Texto completo: | https://hdl.handle.net/10216/135459 |
| País: | Portugal |
| Oai: | oai:repositorio-aberto.up.pt:10216/135459 |