这里的论文主要
论文原文:
Deep generative models have had less of an impact, due to the difficulty of approximating many intractable probabilistic computations that arise in maximum likelihood estimation and related strategies, and due to difficulty of leveraging the benefits of piecewise linear units in the generative context. We propose a new generative model estimation procedure that sidesteps these difficulties.
讲述了(1)用出现在最大似然估计和相关策略很难很好的完成许多棘手的概率近似计算,(2)同时也很难扩充分段的网络结构的优势去生成那些相应内容
论文原文:
论文原文:
注意上面的式子,也就变成了下面这个 GAN定义的判别器loss,我们可以得到最优判别器的形式;而在最优判别器的下,我们可以把原始GAN定义的生成器loss等价变换为最小化真实分布P_r与生成分布P_g之间的JS散度。我们越训练判别器,它就越接近最优,最小化生成器的loss也就会越近似于最小化P_r和P_g之间的JS散度。 但其实在散度的表示存在很多问题,详细见:Wasserstein-GAN
论文原文:
在这里注意的是可以理解为在优化最佳判别器的时候同时在给生成器最佳的梯度,简单理解就是在判别器在判别的时候,它在同时告诉生成器,应该要怎么生成一张图片或其他来让判别器认为生成的图片更加的真实。
缺点:必须同时把握好训练强度,避免生成样本过度接近真实样本从而减少其多样性。也就是too many values of z to the same value of x to have enough diversity to model p data
优点:(1)不需要马尔可夫链,反馈有反向梯度的形式,(2)对抗最大的好处是判别器的梯度之间传向生成器(3)生成对抗网络甚至可以拟合degenerate的分布。
GANs provide an attractive alternative to maximum likelihood techniques. One can additionally argue that their learning process and the lack of a heuristic cost function (such as pixel-wise independent mean-square error) are attractive to representation learning. GANs have been known to be unstable to train, often resulting in generators that produce nonsensical outputs. There has been very limited published research in trying to understand and visualize what GANs learn, and the intermediate representations of multi-layer GANs.
首先来看下它的结构:
We propose and evaluate a set of constraints on the architectural topology of Convolutional GANs that make them stable to train in most settings. We name this class of architectures Deep Convolutional GANs (DCGAN) • We use the trained discriminators for image classification tasks, showing competitive performance with other unsupervised algorithms. • We visualize the filters learnt by GANs and empirically show that specific filters have learned to draw specific objects.
• We show that the generators have interesting vector arithmetic properties allowing for easy manipulation of many semantic qualities of generated samples.
也就是取消pooling,取消全链接,加上BN。