Tuan Anh Le

importance weighted autoencoders

19 February 2017

notes on (Burda, Grosse, & Salakhutdinov, 2016).

summary

understanding: 8/10
code: https://github.com/yburda/iwae

importance weighted autoencoders (iwae) improve variational autoencoders. the main difference is in the loss function. iwae uses: \begin{align} \mathcal L_K = \E_{q_{\phi}(x \given y)}\left[\log \frac{1}{K} \sum_{k = 1}^K w_k\right] \end{align} where and . vae uses the same, but always with . both objectives are lower bounds of .

iwae is better because:


references

  1. Burda, Y., Grosse, R., & Salakhutdinov, R. (2016). Importance Weighted Autoencoders. In International Conference on Learning Representations (ICLR).
    @inproceedings{burda2016importance,
      title = {Importance Weighted Autoencoders},
      author = {Burda, Yuri and Grosse, Roger and Salakhutdinov, Ruslan},
      year = {2016},
      booktitle = {International Conference on Learning Representations (ICLR)}
    }
    

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