Sample selection is the most straightforward technique to combat label noise, aiming to distinguish mislabeled samples during training and avoid the degradation of the robustness of the model. In the workflow, $\textit{selecting possibly clean data}$ and $\textit{model update}$ are iterative. However, their interplay and intrinsic characteristics hinder the robustness and efficiency of learning with noisy labels: 1)~The model chooses clean data with selection bias, leading to the accumulated error in the model update. 2) Most selection strategies leverage partner networks or supplementary information to mitigate label corruption, albeit with increased computation resources and lower throughput speed. Therefore, we employ only one network with the jump manner update to decouple the interplay and mine more semantic information from the loss for a more precise selection. Specifically, the selection of clean data for each model update is based on one of the prior models, excluding the last iteration. The strategy of model update exhibits a jump behavior in the form. Moreover, we map the outputs of the network and labels into the same semantic feature space, respectively. In this space, a detailed and simple loss distribution is generated to distinguish clean samples more effectively. Our proposed approach achieves almost up to $2.53\times$ speedup, $0.46\times$ peak memory footprint, and superior robustness over state-of-the-art works with various noise settings.

翻译：样本选择是应对标签噪声最直接的技术，旨在在训练过程中区分错误标注的样本，避免模型鲁棒性下降。在该工作流程中，$\textit{选择可能干净的数据}$与$\textit{模型更新}$是迭代进行的。然而，二者的相互作用及其内在特性阻碍了噪声标签学习的鲁棒性与效率：1)~模型以选择偏差挑选干净数据，导致误差在模型更新中累积；2)大多数选择策略依赖伙伴网络或辅助信息来缓解标签污染，但会消耗更多计算资源并降低吞吐速度。为此，我们仅采用单一网络配合跳跃式更新机制，以解耦二者相互作用，并从损失中挖掘更多语义信息以实现更精确的选择。具体而言，每次模型更新所采用的干净数据选择均基于先前某个模型（排除最近一次迭代）进行。该模型更新策略在形式上表现出跳跃特性。此外，我们分别将网络输出与标签映射至同一语义特征空间，在此空间中生成细致而简洁的损失分布，从而更有效地区分干净样本。所提方法在多种噪声设置下，相比现有最优工作实现了最高约$2.53\times$的加速比、$0.46\times$的峰值内存占用，并展现出更优越的鲁棒性。