Mask-guided Semantic Alignment: Robust Learning with Noisy Labels via Temporal Attention Stability

Noisy labels pose a significant challenge in training deep neu-ral networks, as corrupted annotations inevitably induce overfitting andsignificantly degrade generalization. However, existing methods typicallyrely on static metrics and overlook training dynamics, struggling to effec-tively separate clean samples from noisy ones under complex and highnoise scenarios. In this paper, we propose a temporal attention-basedframework for learning with noisy labels. Specifically, we introduce an at-tention temporal consistency module for reliable sample selection, whichseparates samples by quantifying the evolutionary stability of visual at-tention during training. Then, to address the scarcity of clean samples inhigh-noise scenarios, we design an adaptive attention masks module tosynthesize images from clean and noisy samples, which employs differen-tial masking strategies to generate high-quality training samples. Finally,we present a mask-guided feature alignment module, which introducesfeature consistency regularization to facilitate the learning of robustrepresentations for noisy data. Extensive experiments demonstrate thatour method outperforms state-of-the-art approaches, achieving accuracygains of 1.6%/2.8% on CIFAR-10/100 under 90% symmetric noise, re-spectively. Code is available at https://github.com/iCAN-SZU/MSA