The question of what makes a data distribution suitable for deep learning is a fundamental open problem.

Both component functions are in expectation forms, i.e.,

An important step to understanding and improving artificial vision systems is to measure image similarity purely based on intrinsic object properties that define object identity. This problem has been studied in the computer vision literature as re-identification, though mostly restricted to specific object categories such as people and cars. We propose to extend it to general object categories, exploring an image similarity metric based on object intrinsics.

Subject

For instance, data augmentation consistency regularization [Sohn et al., 2020, Berthelot et al., 2019] generates pseudo-labels from the current model predictions on

BACON allows computational resources to be focused on simulations that provide the most value in terms of reducing epistemic uncertainty.