Systems and methods for mutual information based self-supervised learning
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United States of America
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Jan 14, 2025
Grant Date -
Mar 3, 2022
app pub date -
Aug 28, 2020
filing date -
Aug 28, 2020
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Abstract
Embodiments described herein combine both masked reconstruction and predictive coding. Specifically, unlike contrastive learning, the mutual information between past states and future states are directly estimated. The context information can also be directly captured via shifted masked reconstruction—unlike standard masked reconstruction, the target reconstructed observations are shifted slightly towards the future to incorporate more predictability. The estimated mutual information and shifted masked reconstruction loss can then be combined as the loss function to update the neural model.
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