Dilated causal convolutions for audio and text generation

In today's summary we dive into the architecture of WaveNet and its successor ByteNet which are autoregressive generative models for generating audio and respectively sentences on character-level. The architectures behind both models are based on dilated causal convolutional layers which recently got much attention also in image generation tasks. Especially modeling sequential data with long term dependencies like audio or text seem to benefit from convolutions with dilations to increase the receptive field. Without further introduction we start right away with the main components behind WaveNet, which will later also appear in the architecture of ByteNet. The key ingredient are so called dilated causal convolutions which have some advantages over standard convolutions.