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Maximum Mean Discrepancy Tensorflow, Jul 27, 2020 · MMD的基本思想就是，如果两个随机变量的任意阶都相同的话，那么两个分布就是一致的。 而当两个分布不相同的话，那么使得两个分布之间差距最大的那个矩应该被用来作为度量两个分布的标准。 MMD常被用来度量两个分布之间的距离，是迁移学习中常用的损失函数。 定义如下： [ x , x 2 , x 3 ] [x,x^2,x^3] [x,x2,x3]，那么对应的求期望就相当于分别在求一、二、三阶矩。 然后将他们的上确界作为MMD的值。 注意这里举的例子只是便于理解。 刚才讲到，两个分布应该是由任意阶来描述的，那么 f 应该能够将 x 映射到任意阶上，这里就用到了核技巧，高斯核函数对应的映射函数恰好可以映射到无穷维上。 Mar 8, 2019 · This definition utilize a supremum and a function belonging to a unit ball F in Reproducing Kernel Hilbert Space. datasets to easily fetch a number of datasets for different modalities. The metric guarantees that the result is 0 if and only if the two distributions it is comparing are exactly the same. Maximum mean discrepancy for tensorflow. proto. The Maximum Mean Discrepancy (MMD) is a measure of the distance between the distributions of prediction scores on two groups of examples. Is there any available API in Tensorflow that can apply MMD as loss function directly? If there is not, how can I implement it in Tensorflow so that the gradients can be applied automatically? Thanks for any reply! Adjusted Maximum Mean Discrepancy between predictions on two groups of examples. tensorflow import preprocess_drift. GitHub Gist: instantly share code, notes, and snippets. For detailed installation instructions, see Installation and Requirements. hhg5, 6dh, 09bf, uqxz, wuov, mecr, ngnc59, lo8oyn, to, j5,