# Auxiliary Image Regularization for Deep CNNs with Noisy Labels

arXiv: https://arxiv.org/abs/1511.07069 · HTML: https://mlsanity.com/paper/1511.07069/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 22 Nov 2015.

## Built on by (most cited first, 14 of 14)

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- [Uncertainty-aware Label Distribution Learning for Facial Expression Recognition](https://mlsanity.com/paper/2209.10448/) (2022)
- [Learning with Noisy Labels by Efficient Transition Matrix Estimation to Combat Label Miscorrection](https://mlsanity.com/paper/2111.14932/) (2021)
- [LA-Net: Landmark-Aware Learning for Reliable Facial Expression Recognition under Label Noise](https://mlsanity.com/paper/2307.09023/) (2023)
- [Detecting Label Errors by using Pre-Trained Language Models](https://mlsanity.com/paper/2205.12702/) (2022)
- [Constrained Instance and Class Reweighting for Robust Learning under Label Noise](https://mlsanity.com/paper/2111.05428/) (2021)
- [Clustering Aided Weakly Supervised Training to Detect Anomalous Events in Surveillance Videos](https://mlsanity.com/paper/2203.13704/) (2022)
- [On Universal Black-Box Domain Adaptation](https://mlsanity.com/paper/2104.04665/) (2021)
- [Con4m: Context-aware Consistency Learning Framework for Segmented Time Series Classification](https://mlsanity.com/paper/2408.00041/) (2024)
- [Granular-ball Representation Learning for Deep CNN on Learning with Label Noise](https://mlsanity.com/paper/2409.03254/) (2024)
- [Friends and Foes in Learning from Noisy Labels](https://mlsanity.com/paper/2103.15055/) (2021)
- [Deep Learning with Label Noise: A Hierarchical Approach](https://mlsanity.com/paper/2205.14299/) (2022)
- [Learning advisor networks for noisy image classification](https://mlsanity.com/paper/2211.04177/) (2022)
- [Data Quality in Edge Machine Learning: A State-of-the-Art Survey](https://mlsanity.com/paper/2406.02600/) (2024)

## Abstract (arXiv)

Precisely-labeled data sets with sufficient amount of samples are very important for training deep convolutional neural networks (CNNs). However, many of the available real-world data sets contain erroneously labeled samples and those errors substantially hinder the learning of very accurate CNN models. In this work, we consider the problem of training a deep CNN model for image classification with mislabeled training samples - an issue that is common in real image data sets with tags supplied by amateur users. To solve this problem, we propose an auxiliary image regularization technique, optimized by the stochastic Alternating Direction Method of Multipliers (ADMM) algorithm, that automatically exploits the mutual context information among training images and encourages the model to select reliable images to robustify the learning process. Comprehensive experiments on benchmark data sets clearly demonstrate our proposed regularized CNN model is resistant to label noise in training data.