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A Step Towards Efficient Evaluation of Complex Perception Tasks in Simulation
[article]
2021
arXiv
pre-print
There has been increasing interest in characterising the error behaviour of systems which contain deep learning models before deploying them into any safety-critical scenario. However, characterising such behaviour usually requires large-scale testing of the model that can be extremely computationally expensive for complex real-world tasks. For example, tasks involving compute intensive object detectors as one of their components. In this work, we propose an approach that enables efficient
arXiv:2110.02739v2
fatcat:eby6vdwgpjasle7y4myaadwywi