Constrained instance and class reweighting for robust learning under label noise
Deep neural networks have shown impressive performance in supervised learning, enabled
by their ability to fit well to the provided training data. However, their performance is largely
dependent on the quality of the training data and often degrades in the presence of noise.
We propose a principled approach for tackling label noise with the aim of assigning
importance weights to individual instances and class labels. Our method works by
formulating a class of constrained optimization problems that yield simple closed form …
by their ability to fit well to the provided training data. However, their performance is largely
dependent on the quality of the training data and often degrades in the presence of noise.
We propose a principled approach for tackling label noise with the aim of assigning
importance weights to individual instances and class labels. Our method works by
formulating a class of constrained optimization problems that yield simple closed form …
[CITATION][C] Constrained instance and class reweighting for robust learning under label noise. arXiv preprint
A Kumar, E Amid - arXiv preprint arXiv:2111.05428, 2021
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