Existing studies on the uncertainty quantification for classification via deep neural network have utilized extra parameters for variances without reflecting the functional relationship between a mean and a variance of multinomial random variables. Furthermore, few studies considered decomposition of aleatoric and epistemic uncertainties in classification taking into account the relationship ... Classification uncertainty的研究比较成熟，segmentation、regression(e.g. depth estimation)次之。如果看CV领域的uncertainty，基本上都能追溯到Yarin Gal和他在NIPS2017年发的论文 What Uncertainties Do We Need in Bayesian Deep Learning for Computer Vision? Quantification of uncertainty associated with segmentation tasks offers principled measures to inspect the segmentation output. Realizing its utility in identifying erroneous segmentation and the potential applications in clinical decision making, here we develop a U-Net based drusen segmentation model and quantify the segmentation uncertainty. We investigate epistemic and aleatoric ... The aleatoric uncertainty can be divided into two sub-categories: heteroscedastic and homoscedastic uncertainty. Heteroscedastic uncertainty assumes that the aleatoric uncertainty is data dependent and, thus, that the uncertainty varies over different inputs. Hence, models that can capture the heteroscedastic uncertainty are useful when the uncertainty is greater in some areas of the input ... Request PDF Aleatoric and Epistemic Uncertainty in Machine Learning: A Tutorial Introduction The notion of uncertainty is of major importance in machine learning and constitutes a key element ... Heteroscedastic aleatoric uncertainty. Exemplary, see the x-axis as a time scale from 8am to 10pm and we measure our heart rate over one week.We first take measurements in the morning at 8am just after getting up, another one at 10am after you have arrived in office after having cycled for 20 minutes and one in the evening at 6pm before you leave your office. Aleatoric uncertainty captures our uncertainty with respect to information which our data cannot explain. For example, aleatoric uncertainty in images can be attributed to occlusions (because cameras can’t see through objects) or lack of visual features or over-exposed regions of an image, etc. It can be explained away with the ability to observe all explanatory variables with increasing ... As already said, aleatoric uncertainty is typically understood as uncertainty that is due to influences on the data-generating process that are inherently random, that is, due to the non-deterministic nature of the sought input/output dependency. This part of the uncertainty is irreducible, in the sense that the learner cannot get rid of it. Model uncertainty and approximation uncertainty, on ... Classification with Uncertainty Modeling uncertainty for classification Add noise to the output of the network (logits) Variance of this noise depends on the input If model is wrong, bigger uncertainty results in a lower loss. Classification with Uncertainty Example. Classification with Uncertainty Network outputs: First class: 1 Second class: 2. Classification with Uncertainty After softmax ... In contrast to previous works focusing mainly on classification or regression-related uncertainty estimation, and recent works of Nair et al. and Roy et al. investigating only test-time dropout-based (epistemic) uncertainty for segmentation, we extensively investigate different kinds of uncertainties for CNN-based medical image segmentation, including not only epistemic but also aleatoric ...
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