# SemiNLL: A Framework of Noisy-Label Learning by Semi-Supervised Learning

arXiv: https://arxiv.org/abs/2012.00925 · HTML: https://mlsanity.com/paper/2012.00925/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 2 Dec 2020.

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

- [Federated Learning from Pre-Trained Models: A Contrastive Learning Approach](https://mlsanity.com/paper/2209.10083/) (2022)
- [Detecting Corrupted Labels Without Training a Model to Predict](https://mlsanity.com/paper/2110.06283/) (2021)
- [Is one annotation enough? A data-centric image classification benchmark for noisy and ambiguous label estimation](https://mlsanity.com/paper/2207.06214/) (2022)
- [Towards Understanding Deep Learning from Noisy Labels with Small-Loss Criterion](https://mlsanity.com/paper/2106.09291/) (2021)
- [To Aggregate or Not? Learning with Separate Noisy Labels](https://mlsanity.com/paper/2206.07181/) (2022)
- [The Rich Get Richer: Disparate Impact of Semi-Supervised Learning](https://mlsanity.com/paper/2110.06282/) (2021)
- [MSA-GCN:Multiscale Adaptive Graph Convolution Network for Gait Emotion Recognition](https://mlsanity.com/paper/2209.08988/) (2022)
- [A Framework using Contrastive Learning for Classification with Noisy Labels](https://mlsanity.com/paper/2104.09563/) (2021)
- [Label-noise-tolerant medical image classification via self-attention and self-supervised learning](https://mlsanity.com/paper/2306.09718/) (2023)
- [Targeted Augmentation for Low-Resource Event Extraction](https://mlsanity.com/paper/2405.08729/) (2024)
- [Semi-Supervised Cascaded Clustering for Classification of Noisy Label Data](https://mlsanity.com/paper/2205.02209/) (2022)

## Abstract (arXiv)

Deep learning with noisy labels is a challenging task. Recent prominent methods that build on a specific sample selection (SS) strategy and a specific semi-supervised learning (SSL) model achieved state-of-the-art performance. Intuitively, better performance could be achieved if stronger SS strategies and SSL models are employed. Following this intuition, one might easily derive various effective noisy-label learning methods using different combinations of SS strategies and SSL models, which is, however, reinventing the wheel in essence. To prevent this problem, we propose SemiNLL, a versatile framework that combines SS strategies and SSL models in an end-to-end manner. Our framework can absorb various SS strategies and SSL backbones, utilizing their power to achieve promising performance. We also instantiate our framework with different combinations, which set the new state of the art on benchmark-simulated and real-world datasets with noisy labels.