# EntAugment: Entropy-Driven Adaptive Data Augmentation Framework for Image Classification

arXiv: https://arxiv.org/abs/2409.06290 · HTML: https://mlsanity.com/paper/2409.06290/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- Builds on 38 of its 56 references that are in the map.
- First posted on arXiv 10 Sep 2024.

## Builds on (most cited first, 30 of 38)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [mixup: Beyond Empirical Risk Minimization](https://mlsanity.com/paper/1710.09412/) (2017)
- [Wide Residual Networks](https://mlsanity.com/paper/1605.07146/) (2016)
- [CutMix: Regularization Strategy to Train Strong Classifiers with Localizable Features](https://mlsanity.com/paper/1905.04899/) (2019)
- [RandAugment: Practical automated data augmentation with a reduced search space](https://mlsanity.com/paper/1909.13719/) (2019)
- [Fine-Grained Visual Classification of Aircraft](https://mlsanity.com/paper/1306.5151/) (2013)
- [Improved Regularization of Convolutional Neural Networks with Cutout](https://mlsanity.com/paper/1708.04552/) (2017)
- [Random Erasing Data Augmentation](https://mlsanity.com/paper/1708.04896/) (2017)
- [A Comprehensive Survey on Transfer Learning](https://mlsanity.com/paper/1911.02685/) (2019)
- [Large-Scale Long-Tailed Recognition in an Open World](https://mlsanity.com/paper/1904.05160/) (2019)
- [Regularizing Neural Networks by Penalizing Confident Output Distributions](https://mlsanity.com/paper/1701.06548/) (2017)
- [A Downsampled Variant of ImageNet as an Alternative to the CIFAR datasets](https://mlsanity.com/paper/1707.08819/) (2017)
- [On The Power of Curriculum Learning in Training Deep Networks](https://mlsanity.com/paper/1904.03626/) (2019)
- [Curriculum Learning: A Survey](https://mlsanity.com/paper/2101.10382/) (2021)
- [Fast AutoAugment](https://mlsanity.com/paper/1905.00397/) (2019)
- [TrivialAugment: Tuning-free Yet State-of-the-Art Data Augmentation](https://mlsanity.com/paper/2103.10158/) (2021)
- [Hide-and-Seek: Forcing a Network to be Meticulous for Weakly-supervised Object and Action Localization](https://mlsanity.com/paper/1704.04232/) (2017)
- [Understanding the impact of entropy on policy optimization](https://mlsanity.com/paper/1811.11214/) (2018)
- [GridMask Data Augmentation](https://mlsanity.com/paper/2001.04086/) (2020)
- [Adversarial AutoAugment](https://mlsanity.com/paper/1912.11188/) (2019)
- [Image Data Augmentation for Deep Learning: A Survey](https://mlsanity.com/paper/2204.08610/) (2022)
- [Shake-Shake regularization](https://mlsanity.com/paper/1705.07485/) (2017)
- [DADA: Differentiable Automatic Data Augmentation](https://mlsanity.com/paper/2003.03780/) (2020)
- [KeepAugment: A Simple Information-Preserving Data Augmentation Approach](https://mlsanity.com/paper/2011.11778/) (2020)
- [Neuromorphic Data Augmentation for Training Spiking Neural Networks](https://mlsanity.com/paper/2203.06145/) (2022)
- [EfficientTrain: Exploring Generalized Curriculum Learning for Training Visual Backbones](https://mlsanity.com/paper/2211.09703/) (2022)
- [Data Augmentation techniques in time series domain: A survey and taxonomy](https://mlsanity.com/paper/2206.13508/) (2022)
- [TeachAugment: Data Augmentation Optimization Using Teacher Knowledge](https://mlsanity.com/paper/2202.12513/) (2022)
- [Improving Auto-Augment via Augmentation-Wise Weight Sharing](https://mlsanity.com/paper/2009.14737/) (2020)
- [Neuro-Symbolic Entropy Regularization](https://mlsanity.com/paper/2201.11250/) (2022)

## Built on by (most cited first, 13 of 13)

- [A CLIP-Powered Framework for Robust and Generalizable Data Selection](https://mlsanity.com/paper/2410.11215/) (2024)
- [When Dynamic Data Selection Meets Data Augmentation](https://mlsanity.com/paper/2505.03809/) (2025)
- [Sample-aware RandAugment: Search-free Automatic Data Augmentation for Effective Image Recognition](https://mlsanity.com/paper/2508.08004/) (2025)
- [RL-Selector: Reinforcement Learning-Guided Data Selection via Redundancy Assessment](https://mlsanity.com/paper/2506.21037/) (2025)
- [Multimodal-Guided Dynamic Dataset Pruning for Robust and Efficient Data-Centric Learning](https://mlsanity.com/paper/2507.12750/) (2025)
- [Enhancing Small-Scale Dataset Expansion with Triplet-Connection-based Sample Re-Weighting](https://mlsanity.com/paper/2508.07723/) (2025)
- [IPF-RDA: An Information-Preserving Framework for Robust Data Augmentation](https://mlsanity.com/paper/2509.16678/) (2025)
- [On-the-Fly Data Augmentation via Gradient-Guided and Sample-Aware Influence Estimation](https://mlsanity.com/paper/2510.00434/) (2025)
- [Embedding Space Allocation with Angle-Norm Joint Classifiers for Few-Shot Class-Incremental Learning](https://mlsanity.com/paper/2411.09250/) (2024)
- [Pushing the Limits of Distillation-Based Continual Learning via Classifier-Proximal Lightweight Plugins](https://mlsanity.com/paper/2512.03537/) (2025)
- [Data Distribution Valuation Using Generalized Bayesian Inference](https://mlsanity.com/paper/2604.05993/) (2026)
- [Task-Anchored Representation Shaping for Pre-Trained Model-Based Continual Learning](https://mlsanity.com/paper/2608.16345/) (2026)
- [Learning-State-Aware Dynamic Generative Data Augmentation on Small-Scale Datasets](https://mlsanity.com/paper/2608.18907/) (2026)

## Abstract (arXiv)

Data augmentation (DA) has been widely used to improve the generalization of deep neural networks. While existing DA methods have proven effective, they often rely on augmentation operations with random magnitudes to each sample. However, this approach can inadvertently introduce noise, induce distribution shifts, and increase the risk of overfitting. In this paper, we propose EntAugment, a tuning-free and adaptive DA framework. Unlike previous work, EntAugment dynamically assesses and adjusts the augmentation magnitudes for each sample during training, leveraging insights into both the inherent complexities of training samples and the evolving status of deep models. Specifically, in EntAugment, the magnitudes are determined by the information entropy derived from the probability distribution obtained by applying the softmax function to the model's output. In addition, to further enhance the efficacy of EntAugment, we introduce a novel entropy regularization term, EntLoss, which complements the EntAugment approach. Theoretical analysis further demonstrates that EntLoss, compared to traditional cross-entropy loss, achieves closer alignment between the model distributions and underlying dataset distributions. Moreover, EntAugment and EntLoss can be utilized separately or jointly. We conduct extensive experiments across multiple image classification tasks and network architectures with thorough comparisons of existing DA methods. Importantly, the proposed methods outperform others without introducing any auxiliary models or noticeable extra computational costs, highlighting both effectiveness and efficiency. Code is available at https://github.com/Jackbrocp/EntAugment.