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Augmentation types

AugmentationType

Bases: Enum

An enumeration representing the type ofOptimal Transport that can be used in Continuous Flow Matching.

  • EXACT_OT: Standard mini batch optimal transport defined in https://arxiv.org/pdf/2302.00482.
  • EQUIVARIANT_OT: Adding roto/translation optimization to mini batch OT see https://arxiv.org/pdf/2306.15030 https://arxiv.org/pdf/2312.07168 4.2.
  • KABSCH: Simple Kabsch alignment between each data and noise point, No permuation # https://arxiv.org/pdf/2410.22388 Sec 3.2

These prediction types can be used to train neural networks for specific tasks, such as denoising, image synthesis, or time-series forecasting.

Source code in bionemo/moco/interpolants/continuous_time/continuous/data_augmentation/augmentation_types.py 
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class AugmentationType(Enum):
    """An enumeration representing the type ofOptimal Transport that can be used in Continuous Flow Matching.

    - **EXACT_OT**: Standard mini batch optimal transport defined in  https://arxiv.org/pdf/2302.00482.
    - **EQUIVARIANT_OT**: Adding roto/translation optimization to mini batch OT see https://arxiv.org/pdf/2306.15030  https://arxiv.org/pdf/2312.07168 4.2.
    - **KABSCH**: Simple Kabsch alignment between each data and noise point, No permuation # https://arxiv.org/pdf/2410.22388 Sec 3.2

    These prediction types can be used to train neural networks for specific tasks, such as denoising, image synthesis, or time-series forecasting.
    """

    EXACT_OT = "exact_ot"
    EQUIVARIANT_OT = "equivariant_ot"
    KABSCH = "kabsch"