Question: Why studying reweighting strategy?
Evidence: Adversarial Robustness. The vulnerability of DNN models to adversarial examples has been verified by many existing successful attack methods. To improve model robustness against adversarial attacks, various defense methods have been proposed. Among them, adversarial training has been proven to be one of the most effective defense methods. Adversarial training can be formulated as solving a min-max optimization problem where the outer minimization process enforces the model to be robust to adversarial examples, generated by the inner maximization process via some existing attacking methods like PGD.
Based on adversarial training, several variants, such as TRADES, MART, have been presented to improve the model's performance further. More details about adversarial robustness can be found in recent surveys.
Since almost all studies of adversarial training are focused on balanced datasets, it's worthwhile to investigate the performance of adversarial training methods on imbalanced training datasets.
Imbalanced Learning. Most existing works of imbalanced training can be roughly classified into two categories, i.e., re-sampling and reweighting. Re-sampling methods aim to reduce imbalance level through either over-sampling examples from under-represented classes or under-sampling examples from well-represented classes. reweighting methods allocate different weights for different classes or even different examples. For example, Focal loss enlarges the weights of wrongly-classified examples while reducing the weights of well-classified examples in the standard cross entropy loss; and LDAM loss regularizes the underrepresented classes more strongly than the well-represented classes to attain good generalization on under-represented classes. More details about imbalanced learning can be found in recent surveys. The majority of existing methods focused on the nature training scenario and their trained models will be crashed when facing adversarial attacks. Hence, in this paper, we develop a novel method that can defend adversarial attacks and achieve well-pleasing performance under imbalance settings.
Imbalanced Learning. Most existing works of imbalanced training can be roughly classified into two categories, i.e., re-sampling and reweighting. Re-sampling methods aim to reduce imbalance level through either over-sampling examples from under-represented classes (Buda et al., 2018; Byrd & Lipton, 2019) or under-sampling examples from well-represented classes (Japkowicz & Stephen, 2002; Drummond et al., 2003; He & Garcia, 2009). reweighting methods allocate different weights for different classes or even different examples. For example, Focal loss (Lin et al., 2017) enlarges the weights of wrongly-classified examples while reducing the weights of well-classified examples in the standard cross entropy loss; and LDAM loss (Cao et al., 2019) regularizes the underrepresented classes more strongly than the well-represented classes to attain good generalization on under-represented classes. More details about imbalanced learning can be found in recent surveys (He & Ma, 2013; Johnson & Khoshgoftaar, 2019). The majority of existing methods focused on the nature training scenario and their trained models will be crashed when facing adversarial attacks (Szegedy et al., 2013; Goodfellow et al., 2014). Hence, in this paper, we develop a novel method that can defend adversarial attacks and achieve well-pleasing performance under imbalance settings.
Answer: As we mentioned in Section 6, reweighting can be regarded as one of two main categories for existing imbalanced learning methods, and several recently proposed reweighting based methods have achieved well-pleasing performance of the imbalanced learning task, such as Focal loss [1] and LDAM loss [2]. These methods can be easily implemented and worked well with deep neural networks. In addition, re-sampling based methods may bring extra challenges for the learning process, like insufficient training data and additional noises. Hence, as the initial effort to investigate the imbalanced adversarial training problem, we start our study from the reweighting strategy and leave other methods as future work.
[1] Lin, Tsung-Yi, et al. "Focal loss for dense object detection." Proceedings of the IEEE international conference on computer vision. 2017.
[2] Cao, Kaidi, et al. "Learning imbalanced datasets with label-distribution-aware margin loss." arXiv preprint arXiv:1906.07413 (2019).