A Multi-Implicit Neural Representation for Fonts

In our experiments, we train an auto-decoder based network which is an 8-layer MLP, and each hidden layer contains 384 neurons. We use the LeakyReLU activation function as the non-linearity. The latent embedding z is a 128-D vector. For better convergence, sharing the spirit from [4], a skip connection is built between inputs and the third hidden layer, i.e., the inputs are concatenated to the output of the third hidden layer. Rather than following the traditional training routine in the reconstruction and interpolation tasks, the training strategy for the generation task is to freeze the learned latent embedding weights after 1000 epochs, such that the training is more stable across glyphs of the same font family.