Noisy labels significantly hinder the accuracy and generalization of machine
learning models, particularly due to ambiguous instance features. Traditional
techniques that attempt to correct noisy labels directly, such as those using
transition matrices, often fail to address the inherent complexities of the
problem sufficiently. In this paper, we introduce EchoAlign, a transformative
paradigm shift in learning from noisy labels. Instead of focusing on label
correction, EchoAlign treats noisy labels ($\tilde{Y}$) as accurate and
modifies corresponding instance features ($X$) to achieve better alignment with
$\tilde{Y}$. EchoAlign's core components are (1) EchoMod: Employing
controllable generative models, EchoMod precisely modifies instances while
maintaining their intrinsic characteristics and ensuring alignment with the
noisy labels. (2) EchoSelect: Instance modification inevitably introduces
distribution shifts between training and test sets. EchoSelect maintains a
significant portion of clean original instances to mitigate these shifts. It
leverages the distinct feature similarity distributions between original and
modified instances as a robust tool for accurate sample selection. This
integrated approach yields remarkable results. In environments with 30%
instance-dependent noise, even at 99% selection accuracy, EchoSelect retains
nearly twice the number of samples compared to the previous best method.
Notably, on three datasets, EchoAlign surpasses previous state-of-the-art
techniques with a substantial improvement.

通过 EchoAlign，将嘈杂的标签视为准确的，并修改相应的实例特征来更好地与嘈杂的标签保持一致，同时保留干净的原始实例以减轻训练和测试集之间的分布差异。