# Automated Cleanup of the ImageNet Dataset by Model Consensus, Explainability and Confident Learning

arXiv: https://arxiv.org/abs/2103.16324 · HTML: https://mlsanity.com/paper/2103.16324/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 18 of its 33 references that are in the map.
- First posted on arXiv 30 Mar 2021.

## Builds on (most cited first, 18 of 18)

- [Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization](https://mlsanity.com/paper/1610.02391/) (2016)
- [The Many Faces of Robustness: A Critical Analysis of Out-of-Distribution Generalization](https://mlsanity.com/paper/2006.16241/) (2020)
- [Enhanced Deep Residual Networks for Single Image Super-Resolution](https://mlsanity.com/paper/1707.02921/) (2017)
- [Do ImageNet Classifiers Generalize to ImageNet?](https://mlsanity.com/paper/1902.10811/) (2019)
- [Random Erasing Data Augmentation](https://mlsanity.com/paper/1708.04896/) (2017)
- [Grad-CAM++: Improved Visual Explanations for Deep Convolutional Networks](https://mlsanity.com/paper/1710.11063/) (2017)
- [Deep Ensembles: A Loss Landscape Perspective](https://mlsanity.com/paper/1912.02757/) (2019)
- [Confident Learning: Estimating Uncertainty in Dataset Labels](https://mlsanity.com/paper/1911.00068/) (2019)
- [Transfusion: Understanding Transfer Learning for Medical Imaging](https://mlsanity.com/paper/1902.07208/) (2019)
- [Are we done with ImageNet?](https://mlsanity.com/paper/2006.07159/) (2020)
- [Lossy Image Compression with Compressive Autoencoders](https://mlsanity.com/paper/1703.00395/) (2017)
- [Towards Fairer Datasets: Filtering and Balancing the Distribution of the People Subtree in the ImageNet Hierarchy](https://mlsanity.com/paper/1912.07726/) (2019)
- [No Classification without Representation: Assessing Geodiversity Issues in Open Data Sets for the Developing World](https://mlsanity.com/paper/1711.08536/) (2017)
- [The Limits and Potentials of Deep Learning for Robotics](https://mlsanity.com/paper/1804.06557/) (2018)
- [From ImageNet to Image Classification: Contextualizing Progress on Benchmarks](https://mlsanity.com/paper/2005.11295/) (2020)
- [Data augmentation instead of explicit regularization](https://mlsanity.com/paper/1806.03852/) (2018)
- [Auditing ImageNet: Towards a Model-driven Framework for Annotating Demographic Attributes of Large-Scale Image Datasets](https://mlsanity.com/paper/1905.01347/) (2019)
- [A Comprehensive Study of ImageNet Pre-Training for Historical Document Image Analysis](https://mlsanity.com/paper/1905.09113/) (2019)

## Built on by (most cited first, 2 of 2)

- [Ambiguous Annotations: When is a Pedestrian not a Pedestrian?](https://mlsanity.com/paper/2405.08794/) (2024)
- [Understanding Data Influence with Differential Approximation](https://mlsanity.com/paper/2508.14648/) (2025)

## Abstract (arXiv)

The convolutional neural networks (CNNs) trained on ILSVRC12 ImageNet were the backbone of various applications as a generic classifier, a feature extractor or a base model for transfer learning. This paper describes automated heuristics based on model consensus, explainability and confident learning to correct labeling mistakes and remove ambiguous images from this dataset. After making these changes on the training and validation sets, the ImageNet-Clean improves the model performance by 2-2.4 % for SqueezeNet and EfficientNet-B0 models. The results support the importance of larger image corpora and semi-supervised learning, but the original datasets must be fixed to avoid transmitting their mistakes and biases to the student learner. Further contributions describe the training impacts of widescreen input resolutions in portrait and landscape orientations. The trained models and scripts are published on Github (https://github.com/kecsap/imagenet-clean) to clean up ImageNet and ImageNetV2 datasets for reproducible research.