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A collection of literature after or concurrent with Masked Autoencoder (MAE) (Kaiming He el al.).

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Awesome Masked Autoencoders

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Fig. 1. Masked Autoencoders from Kaiming He et al.

Masked Autoencoder (MAE, Kaiming He et al.) has renewed a surge of interest due to its capacity to learn useful representations from rich unlabeled data. Until recently, MAE and its follow-up works have advanced the state-of-the-art and provided valuable insights in research (particularly vision research). Here I list several follow-up works after or concurrent with MAE to inspire future research.

*:octocat: code link, 🌐 project page

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There has been a surge of language research focused on such masking-and-predicting paradigm, e.g. BERT, so I'm not going to report these works.

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A collection of literature after or concurrent with Masked Autoencoder (MAE) (Kaiming He el al.).

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