The machine learning generative algorithms such as GAN and VAE show impressive results in practice when constructing images similar to those in a training set. However, the generation of new images builds mainly on the understanding of the hidden structure of the training database followed by a mere sampling from a multi-dimensional normal variable. In particular each sample is independent from the other ones and can repeatedly propose same type of images. To cure this drawback we propose a kernel-based measure representation method that can produce new objects from a given target measure by approximating the measure as a whole and even staying away from objects already drawn from that distribution. This ensures a better variety of the produced images. The method is tested on some classic machine learning benchmarks.\end{abstract}
翻译:GAN 和 VAE 等机器学习基因算法在构建与培训组相类似的图像时,实际中取得了令人印象深刻的成果。然而,新图像的生成主要基于对培训数据库隐藏结构的理解,随后仅从多维正常变量中取样。特别是,每个样本独立于其他样本,可以反复提出相同的图像类型。为了纠正这一缺陷,我们提出了一个内核计量法,通过接近整个测量标准,甚至远离已经从该分布中提取的物体,从特定目标测量中产生新对象。这确保了所制作图像的更多样化。该方法通过某些经典机器学习基准进行测试。\end{abstract}