  Deep learning with noisy labels is a challenging task. Recent prominent
methods that build on a specific sample selection (SS) strategy and a specific
semi-supervised learning (SSL) model achieved state-of-the-art performance.
Intuitively, better performance could be achieved if stronger SS strategies and
SSL models are employed. Following this intuition, one might easily derive
various effective noisy-label learning methods using different combinations of
SS strategies and SSL models, which is, however, reinventing the wheel in
essence. To prevent this problem, we propose SemiNLL, a versatile framework
that combines SS strategies and SSL models in an end-to-end manner. Our
framework can absorb various SS strategies and SSL backbones, utilizing their
power to achieve promising performance. We also instantiate our framework with
different combinations, which set the new state of the art on
benchmark-simulated and real-world datasets with noisy labels.
