# A Data-Centric Perspective on the Influence of Image Data Quality in Machine Learning Models

arXiv: https://arxiv.org/abs/2509.24420 · HTML: https://mlsanity.com/paper/2509.24420/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 18 of its 35 references that are in the map.
- First posted on arXiv 29 Sep 2025.

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

- [High-Resolution Image Synthesis with Latent Diffusion Models](https://mlsanity.com/paper/2112.10752/) (2021)
- [Beyond a Gaussian Denoiser: Residual Learning of Deep CNN for Image Denoising](https://mlsanity.com/paper/1608.03981/) (2016)
- [Albumentations: fast and flexible image augmentations](https://mlsanity.com/paper/1809.06839/) (2018)
- [FFDNet: Toward a Fast and Flexible Solution for CNN based Image Denoising](https://mlsanity.com/paper/1710.04026/) (2017)
- [Pervasive Label Errors in Test Sets Destabilize Machine Learning Benchmarks](https://mlsanity.com/paper/2103.14749/) (2021)
- [Data-centric Artificial Intelligence: A Survey](https://mlsanity.com/paper/2303.10158/) (2023)
- [Deep Image Deblurring: A Survey](https://mlsanity.com/paper/2201.10700/) (2022)
- [DataPerf: Benchmarks for Data-Centric AI Development](https://mlsanity.com/paper/2207.10062/) (2022)
- [CIFAKE: Image Classification and Explainable Identification of AI-Generated Synthetic Images](https://mlsanity.com/paper/2303.14126/) (2023)
- [Image Denoising: The Deep Learning Revolution and Beyond -- A Survey Paper --](https://mlsanity.com/paper/2301.03362/) (2023)
- [NTIRE 2020 Challenge on Real Image Denoising: Dataset, Methods and Results](https://mlsanity.com/paper/2005.04117/) (2020)
- [Does CLIP's Generalization Performance Mainly Stem from High Train-Test Similarity?](https://mlsanity.com/paper/2310.09562/) (2023)
- [A Data Quality-Driven View of MLOps](https://mlsanity.com/paper/2102.07750/) (2021)
- [Data-Centric Artificial Intelligence](https://mlsanity.com/paper/2212.11854/) (2022)
- [A Generalization of Otsu's Method and Minimum Error Thresholding](https://mlsanity.com/paper/2007.07350/) (2020)
- [Back to the Basics: Revisiting Out-of-Distribution Detection Baselines](https://mlsanity.com/paper/2207.03061/) (2022)
- [Data Quality Matters: Quantifying Image Quality Impact on Machine Learning Performance](https://mlsanity.com/paper/2503.22375/) (2025)
- [How Quality Affects Deep Neural Networks in Fine-Grained Image Classification](https://mlsanity.com/paper/2405.05742/) (2024)

## Abstract (arXiv)

In machine learning, research has traditionally focused on model development, with relatively less attention paid to training data. As model architectures have matured and marginal gains from further refinements diminish, data quality has emerged as a critical factor. However, systematic studies on evaluating and ensuring dataset quality in the image domain remain limited. This study investigates methods for systematically assessing image dataset quality and examines how various image quality factors influence model performance. Using the publicly available and relatively clean CIFAKE dataset, we identify common quality issues and quantify their impact on training. Building on these findings, we develop a pipeline that integrates two community-developed tools, CleanVision and Fastdup. We analyze their underlying mechanisms and introduce several enhancements, including automatic threshold selection to detect problematic images without manual tuning. Experimental results demonstrate that not all quality issues exert the same level of impact. While convolutional neural networks show resilience to certain distortions, they are particularly vulnerable to degradations that obscure critical visual features, such as blurring and severe downscaling. To assess the performance of existing tools and the effectiveness of our proposed enhancements, we formulate the detection of low-quality images as a binary classification task and use the F1 score as the evaluation metric. Our automatic thresholding method improves the F1 score from 0.6794 to 0.9468 under single perturbations and from 0.7447 to 0.8557 under dual perturbations. For near-duplicate detection, our deduplication strategy increases the F1 score from 0.4576 to 0.7928. These results underscore the effectiveness of our workflow and provide a foundation for advancing data quality assessment in image-based machine learning.