Aleatoric Uncertainty. Aleatoric Uncertainty Aleatoric uncertainty captures the 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). Useful in : Large data situations; Real-time applications, because we can form aleatoric models as a deterministic ... In the Bayesian deep learning literature, a distinction is commonly made between epistemic uncertainty and aleatoric uncertainty (Kendall and Gal 2017). Epistemic uncertainty refers to imperfections in the model - in the limit of infinite data, this kind of uncertainty should be reducible to 0. Aleatoric uncertainty is due to data sampling and measurement processes and does not depend on the size of the dataset. Aleatoric uncertainty is the uncertainty arising from the natural stochasticity of observations. Aleatoric uncertainty cannot be reduced even when more data is provided. When it comes to measurement errors, we call it homoscedastic uncertainty because it is constant for all samples. Input data-dependent uncertainty is known as heteroscedastic uncertainty. 偶然不确定性（Aleatoric uncertainty）：举一个例子，在数据标注时如果出现比较大的标注误差，这个误差不是模型带入的，而是数据本身就存在的，数据集里的bias越大，偶然不确定性就越大。 其中，偶然不确定性可以细分为两类： （1）数据依赖型或异方差不确定性（Data-dependent or Heteroscedastic ... About six months ago, we showed how to create a custom wrapper to obtain uncertainty estimates from a Keras network. Today we present a less laborious, as well faster-running way using tfprobability, the R wrapper to TensorFlow Probability.Like most posts on this blog, this one won’t be short, so let’s quickly state what you can expect in return of reading time. Aleatoric uncertainty. The former is probably the more obvious one. Whenever you are taking multiple measurements under the same circumstances, it’s still quite unlikely to get every time the exact same result. Why is that? For several reasons: If you are using a sensor, every device itself has its accuracy, precision, resolution, etc. In case of a manual lab sample the used technique ... In this case, the model captures the aleatoric uncertainty as well, which is due to irreducible noise in the data, or to the stochastic nature of the process generating the data. In this example, we model the output as a IndependentNormal distribution, with learnable mean and variance parameters. 偶然不确定性和认知不确定性（Aleatoric Uncertainty & Epistemic Uncertainty） 我们先来解释深度学习中存在的两种种类不同的不确定性。 偶然不确定性; 我们初高中学物理的时候，老师肯定提过偶然误差这个词。我们做小车下落测量重力加速度常数的时候，每次获得的值都会有一个上下起伏。这是我们因为 ... It encourages the aleatoric uncertainty for OOD data to be close to a predefined ood_std_noise, a hyper-parameter that we set to a rather low value so that low aleatoric uncertainty is predicted in OOD regions. This makes sense as we can only reasonably estimate aleatoric noise in regions of existing training data. Aleatoric uncertainty. 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 precision. Aleatoric uncertainty is very important to model for:
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