# Deflating Dataset Bias Using Synthetic Data Augmentation

arXiv: https://arxiv.org/abs/2004.13866 · HTML: https://mlsanity.com/paper/2004.13866/

## Key facts

- Cited by 38 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 28 Apr 2020.

## Built on by (most cited first, 30 of 38)

- [The Effects of Regularization and Data Augmentation are Class Dependent](https://mlsanity.com/paper/2204.03632/) (2022)
- [Towards Generalizable Tumor Synthesis](https://mlsanity.com/paper/2402.19470/) (2024)
- [Representation Bias in Data: A Survey on Identification and Resolution Techniques](https://mlsanity.com/paper/2203.11852/) (2022)
- [A survey of synthetic data augmentation methods in computer vision](https://mlsanity.com/paper/2403.10075/) (2024)
- [DetDiffusion: Synergizing Generative and Perceptive Models for Enhanced Data Generation and Perception](https://mlsanity.com/paper/2403.13304/) (2024)
- [Efficacy of Synthetic Data as a Benchmark](https://mlsanity.com/paper/2409.11968/) (2024)
- [Sim-to-Real Domain Adaptation for Lane Detection and Classification in Autonomous Driving](https://mlsanity.com/paper/2202.07133/) (2022)
- [KoMA: Knowledge-driven Multi-agent Framework for Autonomous Driving with Large Language Models](https://mlsanity.com/paper/2407.14239/) (2024)
- [SIMBAR: Single Image-Based Scene Relighting For Effective Data Augmentation For Automated Driving Vision Tasks](https://mlsanity.com/paper/2204.00644/) (2022)
- [Analyzing Tumors by Synthesis](https://mlsanity.com/paper/2409.06035/) (2024)
- [FedSyn: Synthetic Data Generation using Federated Learning](https://mlsanity.com/paper/2203.05931/) (2022)
- [Automated Program Repair: Emerging trends pose and expose problems for benchmarks](https://mlsanity.com/paper/2405.05455/) (2024)
- [GenFormer -- Generated Images are All You Need to Improve Robustness of Transformers on Small Datasets](https://mlsanity.com/paper/2408.14131/) (2024)
- [A Dataless FaceSwap Detection Approach Using Synthetic Images](https://mlsanity.com/paper/2212.02571/) (2022)
- [An investigation of challenges encountered when specifying training data and runtime monitors for safety critical ML applications](https://mlsanity.com/paper/2301.13476/) (2023)
- [Aerial Diffusion: Text Guided Ground-to-Aerial View Translation from a Single Image using Diffusion Models](https://mlsanity.com/paper/2303.11444/) (2023)
- [Challenges and Opportunities for Machine Learning Classification of Behavior and Mental State from Images](https://mlsanity.com/paper/2201.11197/) (2022)
- [Evaluating the Impact of Data Augmentation on Predictive Model Performance](https://mlsanity.com/paper/2412.02108/) (2024)
- [Bias Reduction via Cooperative Bargaining in Synthetic Graph Dataset Generation](https://mlsanity.com/paper/2205.13901/) (2022)
- [QAGAN: Adversarial Approach To Learning Domain Invariant Language Features](https://mlsanity.com/paper/2206.12388/) (2022)
- [Addressing Heterogeneity in Federated Learning: Challenges and Solutions for a Shared Production Environment](https://mlsanity.com/paper/2408.09556/) (2024)
- [DatasetEquity: Are All Samples Created Equal? In The Quest For Equity Within Datasets](https://mlsanity.com/paper/2308.09878/) (2023)
- [Addressing Weak Decision Boundaries in Image Classification by Leveraging Web Search and Generative Models](https://mlsanity.com/paper/2310.19986/) (2023)
- [Detecting Spurious Correlations via Robust Visual Concepts in Real and AI-Generated Image Classification](https://mlsanity.com/paper/2311.01655/) (2023)
- [Spatial Transfer Learning for Estimating PM2.5 in Data-poor Regions](https://mlsanity.com/paper/2404.07308/) (2024)
- [Layer Separation: Adjustable Joint Space Width Images Synthesis in Conventional Radiography](https://mlsanity.com/paper/2502.01972/) (2025)
- [Assessing Generative Models for Structured Data](https://mlsanity.com/paper/2503.20903/) (2025)
- [Revisiting Data Augmentation in Model Compression: An Empirical and Comprehensive Study](https://mlsanity.com/paper/2305.13232/) (2023)
- [Methodology for generating synthetic labeled datasets for visual container inspection](https://mlsanity.com/paper/2306.14584/) (2023)
- [Fair GANs through model rebalancing for extremely imbalanced class distributions](https://mlsanity.com/paper/2308.08638/) (2023)

## Abstract (arXiv)

Deep Learning has seen an unprecedented increase in vision applications since the publication of large-scale object recognition datasets and introduction of scalable compute hardware. State-of-the-art methods for most vision tasks for Autonomous Vehicles (AVs) rely on supervised learning and often fail to generalize to domain shifts and/or outliers. Dataset diversity is thus key to successful real-world deployment. No matter how big the size of the dataset, capturing long tails of the distribution pertaining to task-specific environmental factors is impractical. The goal of this paper is to investigate the use of targeted synthetic data augmentation - combining the benefits of gaming engine simulations and sim2real style transfer techniques - for filling gaps in real datasets for vision tasks. Empirical studies on three different computer vision tasks of practical use to AVs - parking slot detection, lane detection and monocular depth estimation - consistently show that having synthetic data in the training mix provides a significant boost in cross-dataset generalization performance as compared to training on real data only, for the same size of the training set.