# Deep Learning from Noisy Image Labels with Quality Embedding

arXiv: https://arxiv.org/abs/1711.00583 · HTML: https://mlsanity.com/paper/1711.00583/

## Key facts

- Cited by 46 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 2 Nov 2017.

## Built on by (most cited first, 30 of 46)

- [To grok or not to grok: Disentangling generalization and memorization on corrupted algorithmic datasets](https://mlsanity.com/paper/2310.13061/) (2023)
- [Noise-aware Learning from Web-crawled Image-Text Data for Image Captioning](https://mlsanity.com/paper/2212.13563/) (2022)
- [Robust Data Pruning under Label Noise via Maximizing Re-labeling Accuracy](https://mlsanity.com/paper/2311.01002/) (2023)
- [FedNoisy: Federated Noisy Label Learning Benchmark](https://mlsanity.com/paper/2306.11650/) (2023)
- [Evaluating Multi-label Classifiers with Noisy Labels](https://mlsanity.com/paper/2102.08427/) (2021)
- [Mitigating Noisy Correspondence by Geometrical Structure Consistency Learning](https://mlsanity.com/paper/2405.16996/) (2024)
- [Latent Class-Conditional Noise Model](https://mlsanity.com/paper/2302.09595/) (2023)
- [Manifold DivideMix: A Semi-Supervised Contrastive Learning Framework for Severe Label Noise](https://mlsanity.com/paper/2308.06861/) (2023)
- [Learning Discriminative Dynamics with Label Corruption for Noisy Label Detection](https://mlsanity.com/paper/2405.19902/) (2024)
- [NLPrompt: Noise-Label Prompt Learning for Vision-Language Models](https://mlsanity.com/paper/2412.01256/) (2024)
- [Tackling Noisy Labels with Network Parameter Additive Decomposition](https://mlsanity.com/paper/2403.13241/) (2024)
- [Noisy Label Processing for Classification: A Survey](https://mlsanity.com/paper/2404.04159/) (2024)
- [Noise-Tolerant Learning for Audio-Visual Action Recognition](https://mlsanity.com/paper/2205.07611/) (2022)
- [Learning from Noisy Labels via Dynamic Loss Thresholding](https://mlsanity.com/paper/2104.02570/) (2021)
- [Truth Discovery in Sequence Labels from Crowds](https://mlsanity.com/paper/2109.04470/) (2021)
- [Robust Meta-learning with Sampling Noise and Label Noise via Eigen-Reptile](https://mlsanity.com/paper/2206.01944/) (2022)
- [On Image Segmentation With Noisy Labels: Characterization and Volume Properties of the Optimal Solutions to Accuracy and Dice](https://mlsanity.com/paper/2206.06484/) (2022)
- [FedDebug: Systematic Debugging for Federated Learning Applications](https://mlsanity.com/paper/2301.03553/) (2023)
- [Learning with Group Noise](https://mlsanity.com/paper/2103.09468/) (2021)
- [Generative Reasoning Integrated Label Noise Robust Deep Image Representation Learning](https://mlsanity.com/paper/2212.01261/) (2022)
- [Multiplicative Reweighting for Robust Neural Network Optimization](https://mlsanity.com/paper/2102.12192/) (2021)
- [How Does Heterogeneous Label Noise Impact Generalization in Neural Nets?](https://mlsanity.com/paper/2106.15475/) (2021)
- [GenKL: An Iterative Framework for Resolving Label Ambiguity and Label Non-conformity in Web Images Via a New Generalized KL Divergence](https://mlsanity.com/paper/2307.09810/) (2023)
- [LM-mixup: Text Data Augmentation via Language Model based Mixup](https://mlsanity.com/paper/2510.20449/) (2025)
- [Collaborative Label Correction via Entropy Thresholding](https://mlsanity.com/paper/2103.17008/) (2021)
- [Bayesian Statistics Guided Label Refurbishment Mechanism: Mitigating Label Noise in Medical Image Classification](https://mlsanity.com/paper/2106.12284/) (2021)
- [Compensation Learning](https://mlsanity.com/paper/2107.11921/) (2021)
- [Efficient and Robust Quantization-aware Training via Adaptive Coreset Selection](https://mlsanity.com/paper/2306.07215/) (2023)
- [Rethinking Noisy Label Learning in Real-world Annotation Scenarios from the Noise-type Perspective](https://mlsanity.com/paper/2307.16889/) (2023)
- [Learning from Different Samples: A Source-free Framework for Semi-supervised Domain Adaptation](https://mlsanity.com/paper/2411.06665/) (2024)

## Abstract (arXiv)

There is an emerging trend to leverage noisy image datasets in many visual recognition tasks. However, the label noise among the datasets severely degenerates the mboxperformance of deep learning approaches. Recently, one mainstream is to introduce the latent label to handle label noise, which has shown promising improvement in the network designs. Nevertheless, the mismatch between latent labels and noisy labels still affects the predictions in such methods. To address this issue, we propose a quality embedding model, which explicitly introduces a quality variable to represent the trustworthiness of noisy labels. Our key idea is to identify the mismatch between the latent and noisy labels by embedding the quality variables into different subspaces, which effectively minimizes the noise effect. At the same time, the high-quality labels is still able to be applied for training. To instantiate the model, we further propose a Contrastive-Additive Noise network (CAN), which consists of two important layers: (1) the contrastive layer estimates the quality variable in the embedding space to reduce noise effect; and (2) the additive layer aggregates the prior predictions and noisy labels as the posterior to train the classifier. Moreover, to tackle the optimization difficulty, we deduce an SGD algorithm with the reparameterization tricks, which makes our method scalable to big data. We conduct the experimental evaluation of the proposed method over a range of noisy image datasets. Comprehensive results have demonstrated CAN outperforms the state-of-the-art deep learning approaches.