Q: In this task, you are given a part of an article. Your task is to generate headline (title) for this text. Preferred headlines are under fifteen words.
We train a generative convolutional neural network<lb>which is able to generate images of objects given object<lb>type, viewpoint, and color. We train the network in a su-<lb>pervised manner on a dataset of rendered 3D chair mod-<lb>els. Our experiments show that the network does not merely<lb>learn all images by heart, but rather finds a meaningful<lb>representation of a 3D chair model allowing it to assess<lb>the similarity of different chairs, interpolate between given<lb>viewpoints to generate the missing ones, or invent new chair<lb>styles by interpolating between chairs from the training set.<lb>We show that the network can be used to find correspon-<lb>dences between different chairs from the dataset, outper-<lb>forming existing approaches on this task.
A:
Learning to Generate Chairs with Convolutional Neural Networks