2016年12月6日【杜克大学Prof. Lawrence Carin来访并作报告】Variational Autoencoder for Deep Learning of Images, Labels and Captions

发布者:王晓娟发布时间:2016-12-06浏览次数:163




报告人:Prof. Lawrence Carin, Duke University

报告题目:Variational Autoencoder for Deep Learning of Images, Labels and  Captions

报告摘要:A novel variational autoencoder is developed to model images, as well as  associated labels or captions. The Deep Generative Deconvolutional Network  (DGDN) is used as a decoder of the latent image features, and a deep  Convolutional Neural Network (CNN) is used as an image encoder; the CNN is used  to approximate a distribution for the latent DGDN features/code. The latent code  is also linked to generative models for labels (Bayesian support vector machine)  or captions (recurrent neural network). When predicting a label/caption for a  new image at test, averaging is performed across the distribution of latent  codes; this is computationally efficient as a consequence of the learned  CNN-based encoder. Since the framework is capable of modeling the image in the  presence/absence of associated labels/captions, a new semi-supervised setting is  manifested for CNN learning with images; the framework even allows unsupervised  CNN learning, based on images alone.

地点:光华楼东辅楼101

时间:2016.12.06, 10:00 am