Boosting Model Resilience via Implicit Adversarial Data Augmentation

• URL: http://arxiv.org/abs/2404.16307v2
• Date: Sat, 1 Jun 2024 06:20:54 GMT
• Title: Boosting Model Resilience via Implicit Adversarial Data Augmentation
• Authors: Xiaoling Zhou, Wei Ye, Zhemg Lee, Rui Xie, Shikun Zhang,
• Abstract summary: We propose to augment the deep features of samples by incorporating adversarial and anti-adversarial perturbation distributions. We then theoretically reveal that our augmentation process approximates the optimization of a surrogate loss function. We conduct extensive experiments across four common biased learning scenarios.
• Score: 20.768174896574916
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Data augmentation plays a pivotal role in enhancing and diversifying training data. Nonetheless, consistently improving model performance in varied learning scenarios, especially those with inherent data biases, remains challenging. To address this, we propose to augment the deep features of samples by incorporating their adversarial and anti-adversarial perturbation distributions, enabling adaptive adjustment in the learning difficulty tailored to each sample's specific characteristics. We then theoretically reveal that our augmentation process approximates the optimization of a surrogate loss function as the number of augmented copies increases indefinitely. This insight leads us to develop a meta-learning-based framework for optimizing classifiers with this novel loss, introducing the effects of augmentation while bypassing the explicit augmentation process. We conduct extensive experiments across four common biased learning scenarios: long-tail learning, generalized long-tail learning, noisy label learning, and subpopulation shift learning. The empirical results demonstrate that our method consistently achieves state-of-the-art performance, highlighting its broad adaptability.

Related papers

• Granularity Matters in Long-Tail Learning [62.30734737735273]
  We offer a novel perspective on long-tail learning, inspired by an observation: datasets with finer granularity tend to be less affected by data imbalance. We introduce open-set auxiliary classes that are visually similar to existing ones, aiming to enhance representation learning for both head and tail classes. To prevent the overwhelming presence of auxiliary classes from disrupting training, we introduce a neighbor-silencing loss.
  arXiv  Detail & Related papers  (2024-10-21T13:06:21Z)
• Feature Augmentation for Self-supervised Contrastive Learning: A Closer Look [28.350278251132078]
  We propose a unified framework to conduct data augmentation in the feature space, known as feature augmentation. This strategy is domain-agnostic, which augments similar features to the original ones and thus improves the data diversity.
  arXiv  Detail & Related papers  (2024-10-16T09:25:11Z)
• AdaAugment: A Tuning-Free and Adaptive Approach to Enhance Data Augmentation [12.697608744311122]
  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.
  arXiv  Detail & Related papers  (2024-05-19T06:54:03Z)
• On Counterfactual Data Augmentation Under Confounding [30.76982059341284]
  Counterfactual data augmentation has emerged as a method to mitigate confounding biases in the training data. These biases arise due to various observed and unobserved confounding variables in the data generation process. We show how our simple augmentation method helps existing state-of-the-art methods achieve good results.
  arXiv  Detail & Related papers  (2023-05-29T16:20:23Z)
• 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)
• SURF: Semi-supervised Reward Learning with Data Augmentation for Feedback-efficient Preference-based Reinforcement Learning [168.89470249446023]
  We present SURF, a semi-supervised reward learning framework that utilizes a large amount of unlabeled samples with data augmentation. In order to leverage unlabeled samples for reward learning, we infer pseudo-labels of the unlabeled samples based on the confidence of the preference predictor. Our experiments demonstrate that our approach significantly improves the feedback-efficiency of the preference-based method on a variety of locomotion and robotic manipulation tasks.
  arXiv  Detail & Related papers  (2022-03-18T16:50:38Z)
• Revisiting Consistency Regularization for Semi-Supervised Learning [80.28461584135967]
  We propose an improved consistency regularization framework by a simple yet effective technique, FeatDistLoss. Experimental results show that our model defines a new state of the art for various datasets and settings.
  arXiv  Detail & Related papers  (2021-12-10T20:46:13Z)
• Adversarial Imitation Learning with Trajectorial Augmentation and Correction [61.924411952657756]
  We introduce a novel augmentation method which preserves the success of the augmented trajectories. We develop an adversarial data augmented imitation architecture to train an imitation agent using synthetic experts. Experiments show that our data augmentation strategy can improve accuracy and convergence time of adversarial imitation.
  arXiv  Detail & Related papers  (2021-03-25T14:49:32Z)
• Self-paced Data Augmentation for Training Neural Networks [11.554821454921536]
  We propose a self-paced augmentation to automatically select suitable samples for data augmentation when training a neural network. The proposed method mitigates the deterioration of generalization performance caused by ineffective data augmentation. Experimental results demonstrate that the proposed SPA can improve the generalization performance, particularly when the number of training samples is small.
  arXiv  Detail & Related papers  (2020-10-29T09:13:18Z)
• On the Benefits of Invariance in Neural Networks [56.362579457990094]
  We show that training with data augmentation leads to better estimates of risk and thereof gradients, and we provide a PAC-Bayes generalization bound for models trained with data augmentation. We also show that compared to data augmentation, feature averaging reduces generalization error when used with convex losses, and tightens PAC-Bayes bounds.
  arXiv  Detail & Related papers  (2020-05-01T02:08:58Z)
• 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.