Is autoencoder a transformer?
Abstract: The variational autoencoder(VAE) has been proved to be a most efficient generative model, but its applications in natural language tasks have not been fully developed.
What is conditional variational autoencoder?
Conditional Variational Autoencoder (CVAE) is an extension of Variational Autoencoder (VAE), a generative model that we have studied in the last post. We’ve seen that by formulating the problem of data generation as a bayesian model, we could optimize its variational lower bound to learn the model.
What is the difference between traditional autoencoder and variational autoencoder?
An autoencoder accepts input, compresses it, and then recreates the original input. A variational autoencoder assumes that the source data has some sort of underlying probability distribution (such as Gaussian) and then attempts to find the parameters of the distribution.
How does a transformer work with an encoder?
Transformer Decoder: The decoder shares the same structure as the encoder network, but with an additional multi-head attention layer over the encoder outputs. At each step of generation, the decoder takes in the output of the encoder, as well as each new token that was previously generated.
How does a transformer autoencoder work for music?
We find that the Transformer autoencoder is able to generate not only performances that sound similar to the input, but also accompaniments of melodies that follow a given style, as shown through both quantitative and qualitative experiments as well as a user listening study.
How are transformers used in controllable sequence generation?
The challenge in controllable sequence generation is that Transformers (Vaswani et al., 2017) and their variants excel as language models or in sequence-to-sequence tasks such as translation, but it is less clear as to how they can: (1) learn and (2) incorporate global conditioning information at inference time.
Which is the current state of the art music transformer?
We focus on symbolic music generation, where Music Transformer (Huang et al., 2019b) is the current state-of-the-art in generating high-quality samples that span over a minute in length.