Statistically explainable GAN inversion

生成性对抗网络(GANS)是一种深度学习框架，它学习从潜在空间到数据空间的映射，以生成与训练数据具有相同概率分布的新数据点(通常是图像)。它的反向对偶，GaN反转，旨在将给定的图像反转到预先训练的GaN模型的潜在空间，从而可以通过在潜在空间中编辑其代码来简单地实现图像的编辑。然而，为了实现期望的图像编辑(例如，样式转移、想象力实现或细粒度修改)，理解、建模和推断预先训练的GaN模型的潜在空间的统计结构和随机性是关键。这就是这个项目的目的，这是一个新的跨学科主题，能够激发无数令人兴奋的图像编辑创新和应用。为了达到这个目的，学生将从三个角度进行研究：首先，通过探索潜在空间的非线性降维来发现期望属性的独立潜在因素；其次，从领域知识中建立具有中间先验的可解释细粒度控制模型作为正则化；最后，从潜在空间中推断图像、文本和音频之间的多模式同步的公共子空间。研究领域：人工智能技术统计和应用概率

Generative adversarial networks (GANs) are a deep learning framework that learns a mapping, from a latent space to a data space, to generate new data points (often images) with the same probability distribution as that of the training data. Its reverse dual, GAN inversion, aims to invert a given image back into the latent space of a pretrained GAN model, such that editing of an image can be simply achieved by editing its code in the latent space. However, to achieve desired image editing (e.g. style transfer, imagination realisation or fine-grained modification), it is pivotal to understand, model and infer the statistical structure and randomness of the latent space of a pretrained GAN model. This is the aim of this project, a new and interdisciplinary topic able to inspire numerous exciting image-editing innovations and applications. To achieve the aim, the student will investigate from three perspectives: firstly to discover independent latent factors of the desired attributes by exploring nonlinear dimension reduction of the latent space; secondly to model interpretable fine-grained controls with some intermediate priors from domain knowledge as regularisation; and finally to infer from the latent space a common subspace for multimodal synchronisation between images, text and audios.Research Areas:Artificial intelligence technologies Statistics and applied probability