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Efficient Uncertainty Estimation for Semantic Segmentation in Videos
[article]
2018
arXiv
pre-print
Uncertainty estimation in deep learning becomes more important recently. A deep learning model can't be applied in real applications if we don't know whether the model is certain about the decision or not. Some literature proposes the Bayesian neural network which can estimate the uncertainty by Monte Carlo Dropout (MC dropout). However, MC dropout needs to forward the model N times which results in N times slower. For real-time applications such as a self-driving car system, which needs to
arXiv:1807.11037v1
fatcat:trbnkqwcevdsnara2i2dszsyre