KL散度(相对熵)和交叉熵的区别 相对熵(relative entropy)就是KL散度(Kullback–Leibler divergence),用于衡量两个概率分布之间的差异。 一句话总结的话:KL散度可以被用于计算代价,而在特定情况下最小化KL散度等价于最小化交叉熵。而交叉熵的运算更简单,所以用交叉熵来当做代价。 如何衡量两个事件/分布之间的不同:K...
This article will cover the relationships between thenegative log-likelihood, entropy, softmax vs. sigmoid cross-entropy loss, maximum likelihood estimation, Kullback-Leibler (KL) divergence, logistic regression, and neural networks. If you are not familiar with the connections between these topics, t...
1、KL散度的概念 KL散度(Kullback-Leibler Divergence)一般用于度量两个概率分布函数之间的“距离”,其定义如下(参考文献[2]、[4]): KL\left[ P\left( X \right) || Q \left( X \right) \right] = \sum_{x\i…
KL散度(相对熵)和交叉熵的区别 相对熵(relative entropy)就是KL散度(Kullback–Leibler divergence),用于衡量两个概率分布之间的差异。 一句话总结的话:KL散度可以被用于计算代价,而在特定情况下最小化KL散度等价于最小化交叉熵。而交叉熵的运算更简单,所以用交叉熵来当做代价。 如何衡量两个事件/分布之间的不同:K...
KL散度(Kullback-Leibler divergence),也称为相对熵,是度量两个概率分布之间差异的一个指标。它被广泛应用于机器学习、信息论、统计学等领域。KL散度可以用来衡量两个概率分布之间的相似度或者差异程度。 在统计学中,正数分布是一种常见的分布形式。它可以描述很多实际现象,比如身高、体重等等。然而,负数分布相对较少被...
In particular, the forward KL divergence loss corresponds exactly to the problem of maximum-likelihood estimation which is the primary basis for many supervised learning problems. Reinforcement Learning = Reverse KL Viewing the problem of reinforcement learning as minimizing the reverse KL objective ...
Second, we enhance the latent loss of the variational model by introducing a maximum likelihood estimate in addition to the KL divergence that is commonly used in variational models. This simple extension acts as a stronger regularizer in the variational autoencoder loss function and lets us obtain...
The Kullback-Leibler divergence is KL(P||Q)=∫∞−∞p(x)logp(x)q(x)dxKL(P||Q)=∫−∞∞p(x)logp(x)q(x)dx If you have two hypothesis regarding which distribution is generating the data XX, PP and QQ, then p(x)q(x)p(x)q(x) is the likelihood ratio for testin...
(^Pn;Qθ), and we can use some statistical learning theory plus a lot of handwaving to argue thatθ∗→argminDKL(P||Qθ)(i.e. we've swapped around the limit and argmin operators). In other words,maximum likelihood estimation is equivalent to minimising KL-divergence. IfDKL(P||Q)...
Kullback–Leibler divergence VAE等和高斯分布打交道的生成模型大量用到了KL散度,这里来看一下 官方文档 离散的概率分布P和Q,定义在同一个概率空间,则Q对P的相对熵 D_{\mathrm{KL}}(P \| Q)=\sum_{x \in \mathcal{X}} P(x) \log \left(\frac{P(x)}{Q(x)}\right) ...