主 题: 第25期学术午餐会报名通知
报告人: 15级硕士肖泰洪 (北京大学)
时 间: 2017-11-01 12:00-13:30
地 点: 理科一号楼1560
各位数院研究生同学：
研究生学术午餐会是在学院领导的大力支持下，由研究生会负责组织的系列学术交流活动。午餐会每次邀请一位同学作为主讲人，面向全院各专业背景的研究生介绍自己科研方向的基本问题、概念和方法，并汇报近期的研究成果和进展，是研究生展示自我、促进交流的学术平台。
研究生会已经举办了二十四期活动，我们将于2017年11月1日周三举办第二十五期学术午餐会活动，欢迎感兴趣的老师和同学积极报名参加。
午餐会时间：2017年11月1日（周三）中午12:00-13:30
地点：理科一号楼1560
报告人简介：15级硕士肖泰洪，导师是马尽文老师，方向是应用数学。
报告题目 [Title]：GeneGAN: Learning Object Transfiguration and Attribute Subspace from Unpaired Data
报告摘要[Abstract]：Object Transfiguration generates diverse novel images by replacing an object in the given image with particular objects from exemplar images. It offers fine-grained controls of image generation, and can perform tasks like “put exactly those eyeglasses from image A onto the nose of the person in image B”. However, object transfiguration often requires disentanglement of objects from backgrounds in feature space, which is challenging and previously requires learning from paired training data: two images sharing the same background but with different objects. In this work, we propose a deterministic generative model that learns disentangled feature subspaces by adversarial training. The training data are two unpaired sets of images: a positive set containing images that have some kind of object, and a negative set being the opposite. The model encodes an image into two complement features: one for the object, and the other for the background. The object and background features from a “positive” parent and a “negative” parent, can be recombined to produce four children, of which two are exact reproductions, and the other two are crossbreeds. Minimizing the adversarial loss between crossbreeds and parents will ensure the crossbreeds inherit the specific objects of parents. On the other hand, minimizing the reconstruction loss between reproductions and parents can ensure the completeness of the features. Overall, the object and background features are complete and disentangled representations of images. Moreover, the object features are found to constitute a multidimensional attribute subspace. Experiments on CelebA and Multi-PIE datasets validate the effectiveness of the proposed model on real world data, for generating images with specified eyeglasses, smiling, hair styles, and lighting conditions.
报名方式：请有意参加的老师在2017年10月31日（周二）中午12点前发送邮件至smsxueshu@126.com，我们将回复邮件和您确认，邮件报名方式仅限于老师，有意参加的同学请点击报名链接
https://www.wjx.top/jq/17596969.aspx，谢谢
