arXiv:2508.09697v3 Announce Type: replace Abstract: Noisy labels are inevitable in real-world scenarios. Due to the strong capacity of deep neural networks to memorize corrupted labels, these noisy labels cause significant performance degradation. Existing noise-robust methods have mainly focused on robust loss functions and sample selection, with comparatively limited exploration of dynamic architectural adaptation. In this paper, we rethink the role of model connectivity in the presence of label noise. Intuitively, performance degradation caused by noisy labels stems from the backpropagation
The proliferation of real-world datasets with inherent label noise, coupled with deep learning's susceptibility to memorizing corrupted labels, necessitates more robust training methodologies.
Improving label-noise resistance is critical for deploying reliable AI systems in fields where perfect data annotation is impractical or impossible, unlocking broader applications for deep learning.
This research suggests a shift towards dynamic architectural adaptation rather than solely relying on robust loss functions or sample selection, offering a new pathway for building more resilient AI models.
- · AI researchers
- · Deep learning practitioners
- · Industries with noisy data
- · Data annotation services (relying on imperfect labels)
Deep learning models will become more tolerant to imperfections in training data, requiring less stringent data curation.
This could accelerate the development and deployment of AI in resource-constrained environments or domains with inherently messy data, broadening AI's applicability.
The reduced dependency on perfectly curated datasets might lower barriers to entry for AI development, fostering innovation in new areas.
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Read at arXiv cs.LG