{"type":"ScholarlyArticle","title":"Replicating Active Appearance Model by Generator Network","authors":[{"name":"Tian Han","slug":"tian-han","given":"Tian","family":"Han","affiliation":"University of California , Los Angeles"},{"name":"Jiawen Wu","slug":"jiawen-wu","given":"Jiawen","family":"Wu","affiliation":"Beijing Institute of Technology"},{"name":"Ying Nian Wu","slug":"ying-nian-wu","given":"Ying Nian","family":"Wu","affiliation":"University of California , Los Angeles"}],"abstract":"A recent Cell paper [Chang and Tsao, 2017] reports an interesting discovery. For the face stimuli generated by a pre-trained active appearance model (AAM), the responses of neurons in the areas of the primate brain that are responsible for face recognition exhibit strong linear relationship with the shape variables and appearance variables of the AAM that generates the face stimuli. In this paper, we show that this behavior can be replicated by a deep generative model called the generator network, which assumes that the observed signals are generated by latent random variables via a top-down convolutional neural network. Specifically, we learn the generator network from the face images generated by a pre-trained AAM model using variational auto-encoder, and we show that the inferred latent variables of the learned generator network have strong linear relationship with the shape and appearance variables of the AAM model that generates the face images. Unlike the AAM model that has an explicit shape model where the shape variables generate the control points or landmarks, the generator network has no such shape model and shape variables. Yet the generator network can learn the shape knowledge in the sense that some of the latent variables of the learned generator network capture the shape variations in the face images generated by AAM.","doi":"10.24963/ijcai.2018/305","url":"https://ijcai.org/proceedings/2018/305","pdf":"https://ijcai.org/proceedings/2018/0305.pdf","markdown":"https://ijcai.org/proceedings/2018/305.md","bibtex":"https://ijcai.org/proceedings/2018/bibtex/305","volume":{"slug":"2018","title":"Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2018","url":"https://ijcai.org/proceedings/2018/","year":2018,"location":"Stockholm","dates":"13-19 July 2018","editors":"Jérôme Lang","isbn":"978-0-9992411-2-7","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2018"},"section":"Machine Learning","track":"Main track","number":305,"pages":{"first":2205,"last":2211},"keywords":["Humans and AI: Brain Sciences","Machine Learning: Neural Networks","Machine Learning: Deep Learning","Machine Learning Applications: Applications of Unsupervised Learning","Humans and AI: Cognitive Modeling"],"videos":[],"references":[{"position":1,"raw":"[Blei et al., 2017] David M Blei, Alp Kucukelbir, and Jon D McAuliffe. 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