# Salvage Reusable Samples from Noisy Data for Robust Learning

arXiv: https://arxiv.org/abs/2008.02427 · HTML: https://mlsanity.com/paper/2008.02427/

## Key facts

- Cited by 19 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 6 Aug 2020.

## Built on by (most cited first, 19 of 19)

- [Non-Salient Region Object Mining for Weakly Supervised Semantic Segmentation](https://mlsanity.com/paper/2103.14581/) (2021)
- [Jo-SRC: A Contrastive Approach for Combating Noisy Labels](https://mlsanity.com/paper/2103.13029/) (2021)
- [Webly Supervised Fine-Grained Recognition: Benchmark Datasets and An Approach](https://mlsanity.com/paper/2108.02399/) (2021)
- [Semantically Meaningful Class Prototype Learning for One-Shot Image Semantic Segmentation](https://mlsanity.com/paper/2102.10935/) (2021)
- [Robust Pseudo-label Learning with Neighbor Relation for Unsupervised Visible-Infrared Person Re-Identification](https://mlsanity.com/paper/2405.05613/) (2024)
- [Learning Discriminative Dynamics with Label Corruption for Noisy Label Detection](https://mlsanity.com/paper/2405.19902/) (2024)
- [Foster Adaptivity and Balance in Learning with Noisy Labels](https://mlsanity.com/paper/2407.02778/) (2024)
- [Unlocking the Power of Open Set : A New Perspective for Open-Set Noisy Label Learning](https://mlsanity.com/paper/2305.04203/) (2023)
- [Learning with Imbalanced Noisy Data by Preventing Bias in Sample Selection](https://mlsanity.com/paper/2402.11242/) (2024)
- [A Survey of Dataset Refinement for Problems in Computer Vision Datasets](https://mlsanity.com/paper/2210.11717/) (2022)
- [QMix: Quality-aware Learning with Mixed Noise for Robust Retinal Disease Diagnosis](https://mlsanity.com/paper/2404.05169/) (2024)
- [Differences Between Hard and Noisy-labeled Samples: An Empirical Study](https://mlsanity.com/paper/2307.10718/) (2023)
- [Unleashing the Potential of Open-set Noisy Samples Against Label Noise for Medical Image Classification](https://mlsanity.com/paper/2406.12293/) (2024)
- [Open set label noise learning with robust sample selection and margin-guided module](https://mlsanity.com/paper/2501.04269/) (2025)
- [Exploiting Web Images for Fine-Grained Visual Recognition by Eliminating Noisy Samples and Utilizing Hard Ones](https://mlsanity.com/paper/2101.09412/) (2021)
- [VEIL: Vetting Extracted Image Labels from In-the-Wild Captions for Weakly-Supervised Object Detection](https://mlsanity.com/paper/2303.09608/) (2023)
- [Group Benefits Instances Selection for Data Purification](https://mlsanity.com/paper/2403.15694/) (2024)
- [Locating and Mitigating Gradient Conflicts in Point Cloud Domain Adaptation via Saliency Map Skewness](https://mlsanity.com/paper/2504.15796/) (2025)
- [Combating Noisy Labels through Fostering Self- and Neighbor-Consistency](https://mlsanity.com/paper/2601.12795/) (2026)

## Abstract (arXiv)

Due to the existence of label noise in web images and the high memorization capacity of deep neural networks, training deep fine-grained (FG) models directly through web images tends to have an inferior recognition ability. In the literature, to alleviate this issue, loss correction methods try to estimate the noise transition matrix, but the inevitable false correction would cause severe accumulated errors. Sample selection methods identify clean ("easy") samples based on the fact that small losses can alleviate the accumulated errors. However, "hard" and mislabeled examples that can both boost the robustness of FG models are also dropped. To this end, we propose a certainty-based reusable sample selection and correction approach, termed as CRSSC, for coping with label noise in training deep FG models with web images. Our key idea is to additionally identify and correct reusable samples, and then leverage them together with clean examples to update the networks. We demonstrate the superiority of the proposed approach from both theoretical and experimental perspectives.