  Structured output representation is a generative task explored in computer
vision that often times requires the mapping of low dimensional features to
high dimensional structured outputs. Losses in complex spatial information in
deterministic approaches such as Convolutional Neural Networks (CNN) lead to
uncertainties and ambiguous structures within a single output representation. A
probabilistic approach through deep Conditional Generative Models (CGM) is
presented by Sohn et al. in which a particular model known as the Conditional
Variational Auto-encoder (CVAE) is introduced and explored. While the original
paper focuses on the task of image segmentation, this paper adopts the CVAE
framework for the task of controlled output representation through attributes.
This approach allows us to learn a disentangled multimodal prior distribution,
resulting in more controlled and robust approach to sample generation. In this
work we recreate the CVAE architecture and train it on images conditioned on
various attributes obtained from two image datasets; the Large-scale CelebFaces
Attributes (CelebA) dataset and the Caltech-UCSD Birds (CUB-200-2011) dataset.
We attempt to generate new faces with distinct attributes such as hair color
and glasses, as well as different bird species samples with various attributes.
We further introduce strategies for improving generalized sample generation by
applying a weighted term to the variational lower bound.
