The Variational Autoencoder (VAE) has proven to be an effective model for producing semantically meaningful latent representations for natural data. However, it has thus far seen limited application to sequential data, and, as we demonstrate, existing recurrent VAE models have difficulty modeling sequences with long-term structure. To address this issue, we propose the use of a hierarchical decoder, which first outputs embeddings for subsequences of the input and then uses these embeddings to generate each subsequence independently. This structure encourages the model to utilize its latent code, thereby avoiding the "posterior collapse" problem which remains an issue for recurrent VAEs. We apply this architecture to modeling sequences of musical notes and find that it exhibits dramatically better sampling, interpolation, and reconstruction performance than a "flat" baseline model. An implementation of our "MusicVAE" is available online at this http URL
The hierarchical paragraph autoencoder proposed in @cite_38 has several parallels to our work. They also consider an autoencoder with hierarchical RNNs for the encoder and decoder, where each level in the hierarchy corresponds to natural subsequences in text (e.g., sentences and words). However, they do not impose any constraints on the latent code, and as a result are unable to sample or interpolate between sequences. Our model otherwise differs in its use of a flat bidirectional encoder and lack of autoregressive connections in the first level of the hierarchy.
@cite_38
2758785877
We present a factorized hierarchical variational autoencoder, which learns disentangled and interpretable representations from sequential data without supervision. Specifically, we exploit the multi-scale nature of information in sequential data by formulating it explicitly within a factorized hierarchical graphical model that imposes sequence-dependent priors and sequence-independent priors to different sets of latent variables. The model is evaluated on two speech corpora to demonstrate, qualitatively, its ability to transform speakers or linguistic content by manipulating different sets of latent variables; and quantitatively, its ability to outperform an i-vector baseline for speaker verification and reduce the word error rate by as much as 35 in mismatched train test scenarios for automatic speech recognition tasks.