# CAAP: Class-Dependent Automatic Data Augmentation Based On Adaptive Policies For Time Series

arXiv: https://arxiv.org/abs/2404.00898 · HTML: https://mlsanity.com/paper/2404.00898/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 19 of its 45 references that are in the map.
- First posted on arXiv 1 Apr 2024.

## Builds on (most cited first, 19 of 19)

- [Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization](https://mlsanity.com/paper/1610.02391/) (2016)
- [Generative Adversarial Networks](https://mlsanity.com/paper/2203.00667/) (2022)
- [mixup: Beyond Empirical Risk Minimization](https://mlsanity.com/paper/1710.09412/) (2017)
- [Deep Inside Convolutional Networks: Visualising Image Classification Models and Saliency Maps](https://mlsanity.com/paper/1312.6034/) (2013)
- [RandAugment: Practical automated data augmentation with a reduced search space](https://mlsanity.com/paper/1909.13719/) (2019)
- [Time Series Classification from Scratch with Deep Neural Networks: A Strong Baseline](https://mlsanity.com/paper/1611.06455/) (2016)
- [TrivialAugment: Tuning-free Yet State-of-the-Art Data Augmentation](https://mlsanity.com/paper/2103.10158/) (2021)
- [Time Series Data Augmentation for Deep Learning: A Survey](https://mlsanity.com/paper/2002.12478/) (2020)
- [Deep Learning for ECG Analysis: Benchmarks and Insights from PTB-XL](https://mlsanity.com/paper/2004.13701/) (2020)
- [Population Based Augmentation: Efficient Learning of Augmentation Policy Schedules](https://mlsanity.com/paper/1905.05393/) (2019)
- [SaliencyMix: A Saliency Guided Data Augmentation Strategy for Better Regularization](https://mlsanity.com/paper/2006.01791/) (2020)
- [Faster AutoAugment: Learning Augmentation Strategies using Backpropagation](https://mlsanity.com/paper/1911.06987/) (2019)
- [AugMax: Adversarial Composition of Random Augmentations for Robust Training](https://mlsanity.com/paper/2110.13771/) (2021)
- [The Effects of Regularization and Data Augmentation are Class Dependent](https://mlsanity.com/paper/2204.03632/) (2022)
- [KeepAugment: A Simple Information-Preserving Data Augmentation Approach](https://mlsanity.com/paper/2011.11778/) (2020)
- [TeachAugment: Data Augmentation Optimization Using Teacher Knowledge](https://mlsanity.com/paper/2202.12513/) (2022)
- [CADDA: Class-wise Automatic Differentiable Data Augmentation for EEG Signals](https://mlsanity.com/paper/2106.13695/) (2021)
- [Data Augmentation for Electrocardiograms](https://mlsanity.com/paper/2204.04360/) (2022)
- [Generalizing electrocardiogram delineation -- Training convolutional neural networks with synthetic data augmentation](https://mlsanity.com/paper/2111.12996/) (2021)

## Built on by (most cited first, 1 of 1)

- [CSRA: Controlled Spectral Residual Augmentation for Robust Sepsis Prediction](https://mlsanity.com/paper/2604.14532/) (2026)

## Abstract (arXiv)

Data Augmentation is a common technique used to enhance the performance of deep learning models by expanding the training dataset. Automatic Data Augmentation (ADA) methods are getting popular because of their capacity to generate policies for various datasets. However, existing ADA methods primarily focused on overall performance improvement, neglecting the problem of class-dependent bias that leads to performance reduction in specific classes. This bias poses significant challenges when deploying models in real-world applications. Furthermore, ADA for time series remains an underexplored domain, highlighting the need for advancements in this field. In particular, applying ADA techniques to vital signals like an electrocardiogram (ECG) is a compelling example due to its potential in medical domains such as heart disease diagnostics. We propose a novel deep learning-based approach called Class-dependent Automatic Adaptive Policies (CAAP) framework to overcome the notable class-dependent bias problem while maintaining the overall improvement in time-series data augmentation. Specifically, we utilize the policy network to generate effective sample-wise policies with balanced difficulty through class and feature information extraction. Second, we design the augmentation probability regulation method to minimize class-dependent bias. Third, we introduce the information region concepts into the ADA framework to preserve essential regions in the sample. Through a series of experiments on real-world ECG datasets, we demonstrate that CAAP outperforms representative methods in achieving lower class-dependent bias combined with superior overall performance. These results highlight the reliability of CAAP as a promising ADA method for time series modeling that fits for the demands of real-world applications.