AdaAugment: A Tuning-Free and Adaptive Approach to Enhance Data Augmentation

• URL: http://arxiv.org/abs/2405.11467v2
• Date: Thu, 23 May 2024 12:58:34 GMT
• Title: AdaAugment: A Tuning-Free and Adaptive Approach to Enhance Data Augmentation
• Authors: Suorong Yang, Peijia Li, Xin Xiong, Furao Shen, Jian Zhao,
• Abstract summary: AdaAugment is a tuning-free Adaptive Augmentation method. It dynamically adjusts augmentation magnitudes for individual training samples based on real-time feedback from the target network. It consistently outperforms other state-of-the-art DA methods in effectiveness while maintaining remarkable efficiency.
• Score: 12.697608744311122
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Data augmentation (DA) is widely employed to improve the generalization performance of deep models. However, most existing DA methods use augmentation operations with random magnitudes throughout training. While this fosters diversity, it can also inevitably introduce uncontrolled variability in augmented data, which may cause misalignment with the evolving training status of the target models. Both theoretical and empirical findings suggest that this misalignment increases the risks of underfitting and overfitting. To address these limitations, we propose AdaAugment, an innovative and tuning-free Adaptive Augmentation method that utilizes reinforcement learning to dynamically adjust augmentation magnitudes for individual training samples based on real-time feedback from the target network. Specifically, AdaAugment features a dual-model architecture consisting of a policy network and a target network, which are jointly optimized to effectively adapt augmentation magnitudes. The policy network optimizes the variability within the augmented data, while the target network utilizes the adaptively augmented samples for training. Extensive experiments across benchmark datasets and deep architectures demonstrate that AdaAugment consistently outperforms other state-of-the-art DA methods in effectiveness while maintaining remarkable efficiency.

Related papers

• EntAugment: Entropy-Driven Adaptive Data Augmentation Framework for Image Classification [10.334396596691048]
  We propose EntAugment, a tuning-free and adaptive DA framework. It dynamically assesses and adjusts the augmentation magnitudes for each sample during training. We also introduce a novel entropy regularization term, EntLoss, which complements the EntAugment approach.
  arXiv  Detail & Related papers  (2024-09-10T07:42:47Z)
• Incorporating Supervised Domain Generalization into Data Augmentation [4.14360329494344]
  We propose a method, contrastive semantic alignment(CSA) loss, to improve robustness and training efficiency of data augmentation. Experiments on the CIFAR-100 and CUB datasets show that the proposed method improves the robustness and training efficiency of typical data augmentations.
  arXiv  Detail & Related papers  (2023-10-02T09:20:12Z)
• Consistency Regularization for Generalizable Source-free Domain Adaptation [62.654883736925456]
  Source-free domain adaptation (SFDA) aims to adapt a well-trained source model to an unlabelled target domain without accessing the source dataset. Existing SFDA methods ONLY assess their adapted models on the target training set, neglecting the data from unseen but identically distributed testing sets. We propose a consistency regularization framework to develop a more generalizable SFDA method.
  arXiv  Detail & Related papers  (2023-08-03T07:45:53Z)
• Implicit Counterfactual Data Augmentation for Robust Learning [24.795542869249154]
  This study proposes an Implicit Counterfactual Data Augmentation method to remove spurious correlations and make stable predictions. Experiments have been conducted across various biased learning scenarios covering both image and text datasets.
  arXiv  Detail & Related papers  (2023-04-26T10:36:40Z)
• Efficient and Effective Augmentation Strategy for Adversarial Training [48.735220353660324]
  Adversarial training of Deep Neural Networks is known to be significantly more data-hungry than standard training. We propose Diverse Augmentation-based Joint Adversarial Training (DAJAT) to use data augmentations effectively in adversarial training.
  arXiv  Detail & Related papers  (2022-10-27T10:59:55Z)
• Improving GANs with A Dynamic Discriminator [106.54552336711997]
  We argue that a discriminator with an on-the-fly adjustment on its capacity can better accommodate such a time-varying task. A comprehensive empirical study confirms that the proposed training strategy, termed as DynamicD, improves the synthesis performance without incurring any additional cost or training objectives.
  arXiv  Detail & Related papers  (2022-09-20T17:57:33Z)
• Augmentation-induced Consistency Regularization for Classification [25.388324221293203]
  We propose a consistency regularization framework based on data augmentation, called CR-Aug. CR-Aug forces the output distributions of different sub models generated by data augmentation to be consistent with each other. We implement CR-Aug to image and audio classification tasks and conduct extensive experiments to verify its effectiveness.
  arXiv  Detail & Related papers  (2022-05-25T03:15:36Z)
• Deceive D: Adaptive Pseudo Augmentation for GAN Training with Limited Data [125.7135706352493]
  Generative adversarial networks (GANs) typically require ample data for training in order to synthesize high-fidelity images. Recent studies have shown that training GANs with limited data remains formidable due to discriminator overfitting. This paper introduces a novel strategy called Adaptive Pseudo Augmentation (APA) to encourage healthy competition between the generator and the discriminator.
  arXiv  Detail & Related papers  (2021-11-12T18:13:45Z)
• Generalization in Reinforcement Learning by Soft Data Augmentation [11.752595047069505]
  SOft Data Augmentation (SODA) is a method that decouples augmentation from policy learning. We find SODA to significantly advance sample efficiency, generalization, and stability in training over state-of-the-art vision-based RL methods.
  arXiv  Detail & Related papers  (2020-11-26T17:00:34Z)
• CoDA: Contrast-enhanced and Diversity-promoting Data Augmentation for Natural Language Understanding [67.61357003974153]
  We propose a novel data augmentation framework dubbed CoDA. CoDA synthesizes diverse and informative augmented examples by integrating multiple transformations organically. A contrastive regularization objective is introduced to capture the global relationship among all the data samples.
  arXiv  Detail & Related papers  (2020-10-16T23:57:03Z)
• Generative Data Augmentation for Commonsense Reasoning [75.26876609249197]
  G-DAUGC is a novel generative data augmentation method that aims to achieve more accurate and robust learning in the low-resource setting. G-DAUGC consistently outperforms existing data augmentation methods based on back-translation. Our analysis demonstrates that G-DAUGC produces a diverse set of fluent training examples, and that its selection and training approaches are important for performance.
  arXiv  Detail & Related papers  (2020-04-24T06:12:10Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.