# Efficient Bilevel Optimization for Meta Label Correction in Noisy Label Learning

Source: [Efficient Bilevel Optimization for Meta Label Correction in Noisy Label Learning](<https://www.sota2.com/research/paper/6a0beef1dbc836b87d82c8fb>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2026-05-20T05:00:20.896Z

Authors: Ba Hoang Anh Nguyen, Viet Cuong Ta

Published: 2026-05-18T04:12:14.000Z

## Abstract

Training a deep neural network with noisy labels could reduce data annotation cost but may introduce noise into the learned model. In meta label correction approaches, an additional meta model besides the main model is trained with a small, clean dataset to correct the large, noisy dataset. However, the update of the meta model requires the computation of hypergradients at the inner step of the main model which signif- icantly increases the computational cost. To improve the training efficiency, we first introduce the dynamic barrier gradient descent into standard meta label correction. While this naive extenstion is able to speed up the training process to approximately first- order complexity, it lacks mechanisms to prevent the leakage of noisy signals to the main model and to stabilize the learning of the meta model. Based on this observation, we propose the EBOMLC method, which is designed with three key improvements including one-step inner loop update, mixture upper loss and alignment- aware dynamic barrier. Empirical results on CIFAR-10 and CIFAR-100 demonstrate that EBOMLC consistently outperforms other baselines, especially under high noise rate settings, while reducing training time of the meta label correction approach.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

- [Image Classification on CIFAR-10 (test) (Uniform Noise and Flip Perturbations)](<https://www.sota2.com/research/sota/image-classification-on-cifar-10-test-uniform-noise-and-flip-perturbations.md>): Image Classification; CIFAR-10 (test); Accuracy (Uniform Noise 0%): 92.92.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2605.17833>)

[PDF](<https://arxiv.org/pdf/2605.17833.pdf>)