New Symmetrized Loss Functions Improve Neural Network Training with Noisy Labels
Researchers have developed a mathematical framework for symmetrizing loss functions to make neural networks more robust to mislabeled training data. The approach decomposes any multi-class loss function into symmetric and class-insensitive components, with the multi-class unhinged loss emerging as the unique convex symmetric loss under certain conditions. This work addresses a practical problem in machine learning where obtaining perfectly labeled datasets is expensive and error-prone.
The paper presents a theoretical and practical approach to handling noisy labels in neural network training by leveraging the symmetry condition, which provides formal robustness guarantees. The authors prove that any multi-class loss function can be uniquely decomposed into a symmetric component and a class-insensitive term, and show that symmetrizing cross-entropy loss yields a linear multi-class extension of the unhinged loss with specific required coefficients. They establish that the multi-class unhinged loss is the unique convex symmetric loss and demonstrate its fundamental role as the linear approximation of any symmetric loss at certain points. Additionally, they introduce two new loss functions—SGCE and alpha-MAE—that interpolate between the unhinged loss and Mean Absolute Error while controlling smoothness properties. Experimental validation on standard noisy-label benchmarks demonstrates competitive performance relative to existing robust loss functions.
What's missing
The paper does not discuss computational complexity or training time comparisons between the proposed loss functions and existing methods. Additionally, the specific assumptions required for the theoretical guarantees (mentioned as 'suitable assumptions') are not detailed in the abstract, and the practical impact of these assumptions on real-world datasets remains unclear.
What different sources said
- arXiv cs.LGCenter
Conservation Laws from Data Symmetry in Neural Networks
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