Statistically explainable GAN inversion

生成式对抗网络（gan）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反演，旨在将给定的图像反演回预训练的GAN模型的潜在空间，这样可以通过在潜在空间中编辑其代码来简单地编辑图像。然而，为了实现所需的图像编辑（例如风格转移，想象实现或细粒度修改），理解，建模和推断预训练GAN模型的潜在空间的统计结构和随机性至关重要。这是这个项目的目的，一个新的跨学科的话题，能够激发许多令人兴奋的图像编辑创新和应用。为了实现这一目标，学生将从三个方面进行研究：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，利用领域知识中的一些中间先验对可解释的细粒度控制进行正则化建模；最后从潜在空间中推断出图像、文本和音频之间多模态同步的公共子空间。研究方向：人工智能技术、统计学和应用概率

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