The latest developments in Artificial Intelligence include diffusion generative models, quite popular tools which can produce original images both unconditionally and, in some cases, conditioned by some inputs provided by the user. Apart from implementation details, which are outside the scope of this work, all of the main models used to generate images are substantially based on a common theory which restores a new image from a completely degraded one. In this work we explain how this is possible by focusing on the mathematical theory behind them, i.e. without analyzing in detail the specific implementations and related methods. The aim of this work is to clarify to the interested reader what all this means mathematically and intuitively.
翻译:人造情报系统的最新发展包括传播基因模型,这些非常流行的工具可以无条件地产生原始图像,有时还以用户提供的一些投入为条件。除了实施细节之外,所有用于生成图像的主要模型基本上都基于一个共同理论,该理论将一个完全退化的图像恢复为新图像。在这项工作中,我们解释如何做到这一点,侧重于其背后的数学理论,即不详细分析具体的实施和相关方法。这项工作的目的是向感兴趣的读者澄清所有这一切在数学和直觉上意味着什么。