EPSRC DTP Studentship: Uncovering the "Instincts" of Deep Generative Models for Fair and Unbiased Visual Content Creation

新冠肺炎疫情加速了数字经济的发展。除了业务关键型远程通信软件之外，令人惊讶的是，视觉内容生成支撑着娱乐行业的快速增长。例如，在实施封锁的那一周，抖音在英国的安装量激增了34%。这种激增意味着视觉内容生成在抗击covid -19方面的双重贡献：1)它在自我隔离、封锁甚至宵禁期间保护人们的心理健康。几乎所有6600万英国人都受到了每日升级的限制规定的影响。因此，至关重要的是要保护他们的心理健康，防止他们成为“被忽视的大多数”。2)它创造了更多“非接触式”工作。他说：“我不可能保护每一份工作。这显示出对新工作机会的迫切需求。幸运的是，这种需求可以通过成为视觉内容创作者来满足，他们通过在Patreon、Youtube和Tiktok等平台上发布内容来谋生。然而，高质量的视觉内容很难创建。这促使人工智能（AI）加入游戏。然而，随着现代人工智能的支柱——深度神经网络遭遇可解释性问题，并可能“无意识地产生偏见”，伦理问题也随之出现。例如，杜克大学最近开发的一种超分辨率方法就有很强的种族偏见：它把一张低分辨率的奥巴马脸转换成一张高分辨率的白人脸。随着BAME+日益增长的社会意识，因此，为公平和公正的视觉内容生成量身定制深度生成模型至关重要。而不是将偏见仅仅归咎于不平衡的训练数据集，我们寻求一个优秀的，有才华和雄心勃勃的博士生进行高质量的研究，以满足无偏见的人工智能的要求。具体来说，该项目旨在回答三个研究问题：1)如何发现预训练的深度生成模型的偏差？2)在训练过程中隐含地引入了哪些偏见？3)如何创建公平、无偏的深度生成模型？

The COVID-19 pandemic accelerated the growth of digital economy. Aside from the business-critical remote communication software, it is surprising to see the fast growth of the entertainment industry backboned by visual content generation. For example, there was a 34% surge of UK installation of TikTok for the week when the lockdown was enforced [1]. Such a surge implies a two-fold contribution of visual content generation in fighting COVID-19:1) It protects people's mental health during the self-isolation, lockdown, and even curfew. Almost all the 66 million people in the UK are affected by the daily upgraded restriction rules [2]. Thus, it is critical to protect their mental health and prevent them from being "the ignored majority".2) It creates more "contactless" jobs. "I cannot protect every job." said Rishi Sunak [3]. This reveals an urgent demand for new job opportunities. Fortunately, this demand can be met by becoming visual content creators who earn their livings by publishing contents on platforms like Patreon, Youtube and Tiktok.However, high-quality visual content can be difficult to create. This motivated Artificial Intelligence (AI) to join the game. Nevertheless, ethical concerns arise as deep neural networks, the backbone of modern AI, suffer from the interpretability problem and can be "unconsciously biased". For example, a recent super-resolution method developed by Duke University [4] has a strong racial bias: it converted a low-resolution Obama face to a high-resolution white face [5]. In line with the growing social awareness of BAME+, it is therefore critical to tailor deep generative models for fair and unbiased visual content generation.Instead of ascribing the biases solely to unbalanced training datasets, we seek an outstanding, talented and ambitious PhD student for carrying out high quality research to fulfil the demands of unbiased AI. Specifically, this project aims to answer three research questions:1) How to uncover the biases of pre-trained deep generative models?2) What biases are implicitly introduced during the training process?3) How to create fair and unbiased deep generative models?