Why Mask Reconstruction Pretraining Helps in Downstream Tasks

Created in July 23, 2023

2023   ·   representation-learning   ·   paper-review

Masked reconstruction pretraining — typified by Masked Autoencoders (MAE) in vision and MLM in language — is empirically powerful but theoretically less understood than contrastive methods.

This paper provides a theoretical analysis of why mask reconstruction transfers well to downstream tasks. The key insight is that the reconstruction objective implicitly forces the encoder to learn features that are predictive of held-out signal — features that capture the structure shared across patches/tokens.

The analysis formally connects:

  • The reconstruction loss
  • The structure of the feature space
  • The transferability to downstream classification

This complements the empirical literature with principled justification for why “predict the missing parts” is such a strong pretraining signal.



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