Answers.Body: <p>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. </p>
  
Answers.ContentLicense: CC BY-SA 4.0
Answers.CreationDate: 2019-08-28T15:01:09.963Z
Answers.LastActivityDate: 2019-08-28T15:01:09.963Z
Answers.UserDisplayName: Brian O'Donnell
Body: <p>I recently read <a href="https://arxiv.org/abs/1610.10099" rel="nofollow noreferrer">Bytenet</a> and <a href="https://arxiv.org/abs/1609.03499" rel="nofollow noreferrer">Wavenet</a> 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?</p>
  
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2019-08-28T13:15:09.577Z
Comments.Text: Have you seen this thread https://www.reddit.com/r/MachineLearning/comments/ai4vro/d_anyone_here_use_bytenet_like_architecture_for/ ?
Comments.UserDisplayName: Brian O'Donnell
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2019-08-28T13:32:33.960Z
Comments.Text: David Pollack mentions some characteristics of the two models in his master's thesis: "Musical Genre Classification of Audio" at https://edoc.hu-berlin.de/bitstream/handle/18452/20012/master_pollack_david.pdf?sequence=7
Comments.UserDisplayName: Brian O'Donnell
Tags: <neural-networks><convolutional-neural-networks><comparison><machine-translation>
Title: What are the differences between Bytenet and Wavenet?
UserDisplayName: razvanc92
ThreadText: # What are the differences between Bytenet and Wavenet?
  
  (Asked by: username_0 on 2019-08-28)
  
  I recently read [Bytenet](https://arxiv.org/abs/1610.10099) and [Wavenet](https://arxiv.org/abs/1609.03499) 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?
  
  **Tags:** neural-networks, convolutional-neural-networks, comparison, machine-translation
  
  > **Comments:**
  > **username_1**: Have you seen this thread https://www.reddit.com/r/MachineLearning/comments/ai4vro/d_anyone_here_use_bytenet_like_architecture_for/ ?  
  > **username_1**: David Pollack mentions some characteristics of the two models in his master's thesis: "Musical Genre Classification of Audio" at https://edoc.hu-berlin.de/bitstream/handle/18452/20012/master_pollack_david.pdf?sequence=7  
  
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  ## 1 answers
  
  ### Answer by username_1 on 2019-08-28. Score: 0
  
  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.
  
  
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