What Uncertainties Do We Need in Bayesian Deep Learning ...
can be interpreted as learned attenuation. This makes the loss more robust to noisy data, also giving new state-of-the-art results on segmentation and depth regression ... uncertainty accounts for our ignorance about which model generated our collected data. This is a notably
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