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Despite the critical contributions of discriminative, restorative, and adversarial learning to SSL performance, yet no SSL method simultaneously employs all three learning ingredients. Our proposed DiRA, a novel SSL framework, unites discriminative, restorative, and adversarial learning in a unified manner to collaboratively glean complementary visual information from unlabeled data for fine-grained semantic representation learning.
