# Smart Augmentation - Learning an Optimal Data Augmentation Strategy

arXiv: https://arxiv.org/abs/1703.08383 · HTML: https://mlsanity.com/paper/1703.08383/

## Key facts

- Cited by 54 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 24 Mar 2017.

## Built on by (most cited first, 30 of 54)

- [A Comprehensive Survey of Image Augmentation Techniques for Deep Learning](https://mlsanity.com/paper/2205.01491/) (2022)
- [Global and Local Texture Randomization for Synthetic-to-Real Semantic Segmentation](https://mlsanity.com/paper/2108.02376/) (2021)
- [AutoBalance: Optimized Loss Functions for Imbalanced Data](https://mlsanity.com/paper/2201.01212/) (2022)
- [Robust and Accurate Object Detection via Adversarial Learning](https://mlsanity.com/paper/2103.13886/) (2021)
- [A Comprehensive Survey on Data Augmentation](https://mlsanity.com/paper/2405.09591/) (2024)
- [A survey of synthetic data augmentation methods in computer vision](https://mlsanity.com/paper/2403.10075/) (2024)
- [Learning Universal Predictors](https://mlsanity.com/paper/2401.14953/) (2024)
- [Learn2Augment: Learning to Composite Videos for Data Augmentation in Action Recognition](https://mlsanity.com/paper/2206.04790/) (2022)
- [Image Data Augmentation Approaches: A Comprehensive Survey and Future directions](https://mlsanity.com/paper/2301.02830/) (2023)
- [GuidedMixup: An Efficient Mixup Strategy Guided by Saliency Maps](https://mlsanity.com/paper/2306.16612/) (2023)
- [Data augmentation for deep learning based accelerated MRI reconstruction with limited data](https://mlsanity.com/paper/2106.14947/) (2021)
- [A novel multiple instance learning framework for COVID-19 severity assessment via data augmentation and self-supervised learning](https://mlsanity.com/paper/2102.03837/) (2021)
- [Data Scarcity in Recommendation Systems: A Survey](https://mlsanity.com/paper/2312.10073/) (2023)
- [An overview of mixing augmentation methods and augmentation strategies](https://mlsanity.com/paper/2107.09887/) (2021)
- [On Data-Augmentation and Consistency-Based Semi-Supervised Learning](https://mlsanity.com/paper/2101.06967/) (2021)
- [A Survey of Automated Data Augmentation Algorithms for Deep Learning-based Image Classification Tasks](https://mlsanity.com/paper/2206.06544/) (2022)
- [Molecule Joint Auto-Encoding: Trajectory Pretraining with 2D and 3D Diffusion](https://mlsanity.com/paper/2312.03475/) (2023)
- [RangeAugment: Efficient Online Augmentation with Range Learning](https://mlsanity.com/paper/2212.10553/) (2022)
- [BdSL36: A Dataset for Bangladeshi Sign Letters Recognition](https://mlsanity.com/paper/2110.00869/) (2021)
- [PseudoAugment: Learning to Use Unlabeled Data for Data Augmentation in Point Clouds](https://mlsanity.com/paper/2210.13428/) (2022)
- [Instance-Conditioned GAN Data Augmentation for Representation Learning](https://mlsanity.com/paper/2303.09677/) (2023)
- [Leveraging Generative AI for Urban Digital Twins: A Scoping Review on the Autonomous Generation of Urban Data, Scenarios, Designs, and 3D City Models for Smart City Advancement](https://mlsanity.com/paper/2405.19464/) (2024)
- [Addressing Heterogeneity in Federated Learning via Distributional Transformation](https://mlsanity.com/paper/2210.15025/) (2022)
- [First-Order Manifold Data Augmentation for Regression Learning](https://mlsanity.com/paper/2406.10914/) (2024)
- [Data Augmentation Policy Search for Long-Term Forecasting](https://mlsanity.com/paper/2405.00319/) (2024)
- [Controllable Data Augmentation Through Deep Relighting](https://mlsanity.com/paper/2110.13996/) (2021)
- [Data-Efficient Augmentation for Training Neural Networks](https://mlsanity.com/paper/2210.08363/) (2022)
- [Revealing the Two Sides of Data Augmentation: An Asymmetric Distillation-based Win-Win Solution for Open-Set Recognition](https://mlsanity.com/paper/2404.19527/) (2024)
- [Data Augmentation for Object Detection via Differentiable Neural Rendering](https://mlsanity.com/paper/2103.02852/) (2021)
- [Tackling Data Bias in Painting Classification with Style Transfer](https://mlsanity.com/paper/2301.02524/) (2023)

## Abstract (arXiv)

A recurring problem faced when training neural networks is that there is typically not enough data to maximize the generalization capability of deep neural networks(DNN). There are many techniques to address this, including data augmentation, dropout, and transfer learning. In this paper, we introduce an additional method which we call Smart Augmentation and we show how to use it to increase the accuracy and reduce overfitting on a target network. Smart Augmentation works by creating a network that learns how to generate augmented data during the training process of a target network in a way that reduces that networks loss. This allows us to learn augmentations that minimize the error of that network. Smart Augmentation has shown the potential to increase accuracy by demonstrably significant measures on all datasets tested. In addition, it has shown potential to achieve similar or improved performance levels with significantly smaller network sizes in a number of tested cases.