Creating New Chinese Fonts based on Manifold Learning and Adversarial Networks

If we can predict the shape of a soft robot for a given set of control parameters, then we can solve the inverse problem: to find an optimal set of control parameters for a

The goal of our approach is to let the data analyst de- cide how to interactively build the feature vectors out of a set of user-defined splitting of scanpaths and then

Given a new input image and its associated semantic segmentation (i.e., object mask), we perform color transfer to map the input image color histogram to a set of target

To com- pute a font specificity score for each font given the collected data, we use an image specificity based method [JP15], except we con- sider the words provided by

Being a manifold itself, the product space endows the set of maps with a geometry of its own, which we exploit to define map operations in the spectral domain; we also

“FontRNN: Generating Large-scale Chinese Fonts via Recurrent Neural

Another advantage of the power diagram is its link with optimal transport: given a set of seeds and a domain Ω, there exists a set of weights such that the map that associates each

• Given a set of items, each with a cost and a value, then determine the number of each item to include in a collection so that the total cost is less than some given cost and