Publications [#338684] of Lawrence Carin

Papers Published

1. Pu, Y; Gan, Z; Henao, R; Yuan, X; Li, C; Stevens, A; Carin, L, Variational autoencoder for deep learning of images, labels and captions, Advances in Neural Information Processing Systems (January, 2016), pp. 2360-2368
   (last updated on 2024/12/31)

   Abstract:
   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.