Imbalanced Adversarial Training with Reweighting

• URL: http://arxiv.org/abs/2107.13639v1
• Date: Wed, 28 Jul 2021 20:51:36 GMT
• Title: Imbalanced Adversarial Training with Reweighting
• Authors: Wentao Wang, Han Xu, Xiaorui Liu, Yaxin Li, Bhavani Thuraisingham, Jiliang Tang
• Abstract summary: We show that adversarially trained models can suffer much worse performance on under-represented classes, when the training dataset is imbalanced. Traditional reweighting strategies may lose efficacy to deal with the imbalance issue for adversarial training. We propose Separable Reweighted Adversarial Training (SRAT) to facilitate adversarial training under imbalanced scenarios.
• Score: 33.51820466479575
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Adversarial training has been empirically proven to be one of the most effective and reliable defense methods against adversarial attacks. However, almost all existing studies about adversarial training are focused on balanced datasets, where each class has an equal amount of training examples. Research on adversarial training with imbalanced training datasets is rather limited. As the initial effort to investigate this problem, we reveal the facts that adversarially trained models present two distinguished behaviors from naturally trained models in imbalanced datasets: (1) Compared to natural training, adversarially trained models can suffer much worse performance on under-represented classes, when the training dataset is extremely imbalanced. (2) Traditional reweighting strategies may lose efficacy to deal with the imbalance issue for adversarial training. For example, upweighting the under-represented classes will drastically hurt the model's performance on well-represented classes, and as a result, finding an optimal reweighting value can be tremendously challenging. In this paper, to further understand our observations, we theoretically show that the poor data separability is one key reason causing this strong tension between under-represented and well-represented classes. Motivated by this finding, we propose Separable Reweighted Adversarial Training (SRAT) to facilitate adversarial training under imbalanced scenarios, by learning more separable features for different classes. Extensive experiments on various datasets verify the effectiveness of the proposed framework.

Related papers

• Outlier Robust Adversarial Training [57.06824365801612]
  We introduce Outlier Robust Adversarial Training (ORAT) in this work. ORAT is based on a bi-level optimization formulation of adversarial training with a robust rank-based loss function. We show that the learning objective of ORAT satisfies the $mathcalH$-consistency in binary classification, which establishes it as a proper surrogate to adversarial 0/1 loss.
  arXiv  Detail & Related papers  (2023-09-10T21:36:38Z)
• Alleviating the Effect of Data Imbalance on Adversarial Training [26.36714114672729]
  We study adversarial training on datasets that obey the long-tailed distribution. We propose a new adversarial training framework -- Re-balancing Adversarial Training (REAT)
  arXiv  Detail & Related papers  (2023-07-14T07:01:48Z)
• Vulnerability-Aware Instance Reweighting For Adversarial Training [4.874780144224057]
  Adversarial Training (AT) has been found to substantially improve the robustness of deep learning classifiers against adversarial attacks. AT exerts an uneven influence on different classes in a training set and unfairly hurts examples corresponding to classes that are inherently harder to classify. Various reweighting schemes have been proposed that assign unequal weights to robust losses of individual examples in a training set. In this work, we propose a novel instance-wise reweighting scheme. It considers the vulnerability of each natural example and the resulting information loss on its adversarial counterpart occasioned by adversarial attacks.
  arXiv  Detail & Related papers  (2023-07-14T05:31:32Z)
• Enhancing Accuracy and Robustness through Adversarial Training in Class Incremental Continual Learning [0.34265828682659694]
  Adversarial attack to deep learning models is a fatal security issue. CICL is well-known defense method against adversarial attack. We propose External Adversarial Training (EAT) which can be applied to methods using experience replay.
  arXiv  Detail & Related papers  (2023-05-23T04:37:18Z)
• Optimising Equal Opportunity Fairness in Model Training [60.0947291284978]
  Existing debiasing methods, such as adversarial training and removing protected information from representations, have been shown to reduce bias. We propose two novel training objectives which directly optimise for the widely-used criterion of it equal opportunity, and show that they are effective in reducing bias while maintaining high performance over two classification tasks.
  arXiv  Detail & Related papers  (2022-05-05T01:57:58Z)
• Enhancing Adversarial Training with Feature Separability [52.39305978984573]
  We introduce a new concept of adversarial training graph (ATG) with which the proposed adversarial training with feature separability (ATFS) enables to boost the intra-class feature similarity and increase inter-class feature variance. Through comprehensive experiments, we demonstrate that the proposed ATFS framework significantly improves both clean and robust performance.
  arXiv  Detail & Related papers  (2022-05-02T04:04:23Z)
• Analysis and Applications of Class-wise Robustness in Adversarial Training [92.08430396614273]
  Adversarial training is one of the most effective approaches to improve model robustness against adversarial examples. Previous works mainly focus on the overall robustness of the model, and the in-depth analysis on the role of each class involved in adversarial training is still missing. We provide a detailed diagnosis of adversarial training on six benchmark datasets, i.e., MNIST, CIFAR-10, CIFAR-100, SVHN, STL-10 and ImageNet. We observe that the stronger attack methods in adversarial learning achieve performance improvement mainly from a more successful attack on the vulnerable classes.
  arXiv  Detail & Related papers  (2021-05-29T07:28:35Z)
• FAIR: Fair Adversarial Instance Re-weighting [0.7829352305480285]
  We propose a Fair Adrial Instance Re-weighting (FAIR) method, which uses adversarial training to learn instance weighting function that ensures fair predictions. To the best of our knowledge, this is the first model that merges reweighting and adversarial approaches by means of a weighting function that can provide interpretable information about fairness of individual instances.
  arXiv  Detail & Related papers  (2020-11-15T10:48:56Z)
• Robust Pre-Training by Adversarial Contrastive Learning [120.33706897927391]
  Recent work has shown that, when integrated with adversarial training, self-supervised pre-training can lead to state-of-the-art robustness. We improve robustness-aware self-supervised pre-training by learning representations consistent under both data augmentations and adversarial perturbations.
  arXiv  Detail & Related papers  (2020-10-26T04:44:43Z)
• Mitigating Dataset Imbalance via Joint Generation and Classification [17.57577266707809]
  Supervised deep learning methods are enjoying enormous success in many practical applications of computer vision. The marked performance degradation to biases and imbalanced data questions the reliability of these methods. We introduce a joint dataset repairment strategy by combining a neural network classifier with Generative Adversarial Networks (GAN) We show that the combined training helps to improve the robustness of both the classifier and the GAN against severe class imbalance.
  arXiv  Detail & Related papers  (2020-08-12T18:40:38Z)
• Precise Tradeoffs in Adversarial Training for Linear Regression [55.764306209771405]
  We provide a precise and comprehensive understanding of the role of adversarial training in the context of linear regression with Gaussian features. We precisely characterize the standard/robust accuracy and the corresponding tradeoff achieved by a contemporary mini-max adversarial training approach. Our theory for adversarial training algorithms also facilitates the rigorous study of how a variety of factors (size and quality of training data, model overparametrization etc.) affect the tradeoff between these two competing accuracies.
  arXiv  Detail & Related papers  (2020-02-24T19:01:47Z)

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.