outputs/outputs_20230420_114226/main.bbl

\begin{thebibliography}{10}
\providecommand{\natexlab}[1]{#1}
\providecommand{\url}[1]{\texttt{#1}}
\expandafter\ifx\csname urlstyle\endcsname\relax
  \providecommand{\doi}[1]{doi: #1}\else
  \providecommand{\doi}{doi: \begingroup \urlstyle{rm}\Url}\fi

\bibitem[Arkanath~Pathak(2023)]{2303.15533}
Nicholas~Dufour Arkanath~Pathak.
\newblock Sequential training of gans against gan-classifiers reveals
  correlated "knowledge gaps" present among independently trained gan
  instances.
\newblock \emph{arXiv preprint arXiv:2303.15533}, 2023.
\newblock URL \url{http://arxiv.org/abs/2303.15533v1}.

\bibitem[Chanwoo~Kim(2022)]{2212.14149}
Jinhwan Park Wonyong~Sung Chanwoo~Kim, Sathish~Indurti.
\newblock Macro-block dropout for improved regularization in training
  end-to-end speech recognition models.
\newblock \emph{arXiv preprint arXiv:2212.14149}, 2022.
\newblock URL \url{http://arxiv.org/abs/2212.14149v1}.

\bibitem[Dian~Lei(2018)]{1805.08355}
Jianfei~Zhao Dian~Lei, Xiaoxiao~Chen.
\newblock Opening the black box of deep learning.
\newblock \emph{arXiv preprint arXiv:1805.08355}, 2018.
\newblock URL \url{http://arxiv.org/abs/1805.08355v1}.

\bibitem[Hyungrok~Ham(2020)]{2002.02112}
Daeyoung~Kim Hyungrok~Ham, Tae Joon~Jun.
\newblock Unbalanced gans: Pre-training the generator of generative adversarial
  network using variational autoencoder.
\newblock \emph{arXiv preprint arXiv:2002.02112}, 2020.
\newblock URL \url{http://arxiv.org/abs/2002.02112v1}.

\bibitem[Jiyang~Xie \& Jianjun~Lei(2020)Jiyang~Xie and Jianjun~Lei]{2010.05244}
Zhanyu~Ma Jiyang~Xie and Jing-Hao Xue Zheng-Hua Tan Jun~Guo Jianjun~Lei,
  Guoqiang~Zhang.
\newblock Advanced dropout: A model-free methodology for bayesian dropout
  optimization.
\newblock \emph{arXiv preprint arXiv:2010.05244}, 2020.
\newblock URL \url{http://arxiv.org/abs/2010.05244v2}.

\bibitem[Juho~Lee(2018)]{1805.10896}
Jaehong Yoon Hae Beom Lee Eunho Yang Sung Ju~Hwang Juho~Lee, Saehoon~Kim.
\newblock Adaptive network sparsification with dependent variational
  beta-bernoulli dropout.
\newblock \emph{arXiv preprint arXiv:1805.10896}, 2018.
\newblock URL \url{http://arxiv.org/abs/1805.10896v3}.

\bibitem[Wangchunshu~Zhou(2020)]{2004.13342}
Ke~Xu Furu Wei Ming~Zhou Wangchunshu~Zhou, Tao~Ge.
\newblock Scheduled drophead: A regularization method for transformer models.
\newblock \emph{arXiv preprint arXiv:2004.13342}, 2020.
\newblock URL \url{http://arxiv.org/abs/2004.13342v2}.

\bibitem[Weng(2019)]{1904.08994}
Lilian Weng.
\newblock From gan to wgan.
\newblock \emph{arXiv preprint arXiv:1904.08994}, 2019.
\newblock URL \url{http://arxiv.org/abs/1904.08994v1}.

\bibitem[Xu~Shen(2019)]{1911.12675}
Tongliang Liu Fang Xu Dacheng~Tao Xu~Shen, Xinmei~Tian.
\newblock Continuous dropout.
\newblock \emph{arXiv preprint arXiv:1911.12675}, 2019.
\newblock URL \url{http://arxiv.org/abs/1911.12675v1}.

\bibitem[Zhiyuan~Zhang(2021)]{2108.08976}
Ruihan Bao Keiko Harimoto Yunfang Wu Xu~Sun Zhiyuan~Zhang, Wei~Li.
\newblock Asat: Adaptively scaled adversarial training in time series.
\newblock \emph{arXiv preprint arXiv:2108.08976}, 2021.
\newblock URL \url{http://arxiv.org/abs/2108.08976v2}.

\end{thebibliography}