Noise against noise: stochastic label noise helps combat inherent label noise

By Pengfei Chen, Guangyong Chen, Jingwei Zhao, P. Heng. 2021

46 citations recorded by the source corpus.

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

1. Hwanjun Song
2. Minseok Kim
3. Jae-Gil Lee
4. Dongmin Park
5. Yooju Shin
6. Guangyong Chen
7. P. Heng
8. Danruo Deng

Citations in the corpus

• Flattening Sharpness for Dynamic Gradient Projection Memory Benefits Continual Learning (2021, background): “The overfitting problem of DNNs is often attributed to the complex loss landscape containing multiple local optima, and the sharpness of the loss landscape has been widely used to characterize the generalization gap in standard training scenarios from both theoretical and empirical perspectives [27,”
• Learning From Noisy Labels With Deep Neural Networks: A Survey (2020, background): “According to our comprehensive survey, the robustness of deep learning can be enhanced in numerous approaches [16], [25], [68]–[74].”