I recently read Bytenet and Wavenet and I was curious why the first model is not as popular as the second. From my understanding, Bytenet can be seen as a seq2seq model where the encoder and the decoder are similar to Wavenet. Following the trends from NLP where seq2seq models seem to perform better, I find it strange that I couldn't find any paper that compares the two. Are there any drawbacks of Bytenet over Wavenet other than the computation time?
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1$\begingroup$ Have you seen this thread reddit.com/r/MachineLearning/comments/ai4vro/… ? $\endgroup$– Brian O'DonnellAug 28, 2019 at 13:15
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1$\begingroup$ David Pollack mentions some characteristics of the two models in his master's thesis: "Musical Genre Classification of Audio" at edoc.hu-berlin.de/bitstream/handle/18452/20012/… $\endgroup$– Brian O'DonnellAug 28, 2019 at 13:32
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My conclusion is the same as yours that there doesn't seem to be any published comparison of the two models. ByteNet is computationally expensive and requires a lot of parameters. WaveNet improves on ByteNet's efficiency, as you mentioned, and I believe that is the main difference.
