Abstract
Learning from noisy labels (LNL) aims to train high-performance deep models
using noisy datasets. Meta learning based label correction methods have
demonstrated remarkable performance in LNL by designing various meta label
rectification tasks. However, extra clean validation set is a prerequisite for
these methods to perform label correction, requiring extra labor and greatly
limiting their practicality. To tackle this issue, we propose a novel noisy
meta label correction framework STCT, which counterintuitively uses noisy data
to correct label noise, borrowing the spirit in the saying ``Set a Thief to
Catch a Thief''. The core idea of STCT is to leverage noisy data which is
i.i.d. with the training data as a validation set to evaluate model performance
and perform label correction in a meta learning framework, eliminating the need
for extra clean data. By decoupling the complex bi-level optimization in meta
learning into representation learning and label correction, STCT is solved
through an alternating training strategy between noisy meta correction and
semi-supervised representation learning. Extensive experiments on synthetic and
real-world datasets demonstrate the outstanding performance of STCT,
particularly in high noise rate scenarios. STCT achieves 96.9% label correction
and 95.2% classification performance on CIFAR-10 with 80% symmetric noise,
significantly surpassing the current state-of-the-art.