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In recent DL work, consistency regularization consists on ensuring the same output while applying transformations on the input (eg: rotation, translation). Consistency regularization can notably be done with a student-teacher approach.
I'm wondering if we could possibly get inspiration from this approach for our contrast-agnostic segmentation? Eg: further alter the image contrast with non-linear signal scaling (randomize the target shape of the image histogram)?
The text was updated successfully, but these errors were encountered:
In recent DL work, consistency regularization consists on ensuring the same output while applying transformations on the input (eg: rotation, translation). Consistency regularization can notably be done with a student-teacher approach.
I'm wondering if we could possibly get inspiration from this approach for our contrast-agnostic segmentation? Eg: further alter the image contrast with non-linear signal scaling (randomize the target shape of the image histogram)?
The text was updated successfully, but these errors were encountered: