Unsupervised learning

Continual Test-time Adaptation (CTA) is a promising art to secure accuracy gains in continually-changing environments. The state-of-the-art adaptations improve out-of-distribution model accuracy via computation-efficient online test-time gradient …

Recently, self-supervised contrastive pre-training has become the de facto regime, that allows for efficient downstream fine-tuning. Meanwhile, its fairness issues are barely studied, though they have drawn great attention from the machine learning …