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Masked Autoencoders (MAE): The Art of Seeing More by Masking Most & PyTorch Implementation

  • Masked Autoencoders (MAE) scale vision understanding by hiding most of the input image.
  • MAE randomly masks 75% of image for model to reconstruct missing pixels efficiently.
  • MAE introduces asymmetric setup with strong encoder and small decoder for efficient training.
  • MAE's pretraining helps massive models like ViT-H generalize well with ImageNet-1K.

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