# Searching to Exploit Memorization Effect in Learning from Corrupted Labels

arXiv: https://arxiv.org/abs/1911.02377 · HTML: https://mlsanity.com/paper/1911.02377/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 6 Nov 2019.

## Built on by (most cited first, 11 of 11)

- [Selective-Supervised Contrastive Learning with Noisy Labels](https://mlsanity.com/paper/2203.04181/) (2022)
- [Is one annotation enough? A data-centric image classification benchmark for noisy and ambiguous label estimation](https://mlsanity.com/paper/2207.06214/) (2022)
- [Unsupervised Domain Adaptation of Black-Box Source Models](https://mlsanity.com/paper/2101.02839/) (2021)
- [When Noisy Labels Meet Long Tail Dilemmas: A Representation Calibration Method](https://mlsanity.com/paper/2211.10955/) (2022)
- [On the use of Silver Standard Data for Zero-shot Classification Tasks in Information Extraction](https://mlsanity.com/paper/2402.18061/) (2024)
- [Extracting Clean and Balanced Subset for Noisy Long-tailed Classification](https://mlsanity.com/paper/2404.06795/) (2024)
- [Learning from Noisy Labels via Self-Taught On-the-Fly Meta Loss Rescaling](https://mlsanity.com/paper/2412.12955/) (2024)
- [Corrupted but Not Broken: Understanding and Mitigating the Negative Impacts of Corrupted Data in Visual Instruction Tuning](https://mlsanity.com/paper/2502.12635/) (2025)
- [Learning with Silver Standard Data for Zero-shot Relation Extraction](https://mlsanity.com/paper/2211.13883/) (2022)
- [A Curriculum View of Robust Loss Functions](https://mlsanity.com/paper/2305.02139/) (2023)
- [Awareness of uncertainty in classification using a multivariate model and multi-views](https://mlsanity.com/paper/2404.10314/) (2024)

## Abstract (arXiv)

Sample selection approaches are popular in robust learning from noisy labels. However, how to properly control the selection process so that deep networks can benefit from the memorization effect is a hard problem. In this paper, motivated by the success of automated machine learning (AutoML), we model this issue as a function approximation problem. Specifically, we design a domain-specific search space based on general patterns of the memorization effect and propose a novel Newton algorithm to solve the bi-level optimization problem efficiently. We further provide theoretical analysis of the algorithm, which ensures a good approximation to critical points. Experiments are performed on benchmark data sets. Results demonstrate that the proposed method is much better than the state-of-the-art noisy-label-learning approaches, and also much more efficient than existing AutoML algorithms.