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DiRA: Discriminative, Restorative, and Adversarial Learning for Self-supervised Medical Image Analysis
[article]
2022
arXiv
pre-print
Discriminative learning, restorative learning, and adversarial learning have proven beneficial for self-supervised learning schemes in computer vision and medical imaging. Existing efforts, however, omit their synergistic effects on each other in a ternary setup, which, we envision, can significantly benefit deep semantic representation learning. To realize this vision, we have developed DiRA, the first framework that unites discriminative, restorative, and adversarial learning in a unified
arXiv:2204.10437v1
fatcat:pb6rumfdgzfnxhwrqbz76myozu