We design a smoothing robust learning strategy. With linearly augmented adversarial data, balance calibration is imposed by class-aware label smoothing.

We illustrate that data imbalance can indeed lead to in- accurate boundary learning, simultaneously expanding the probability of classification error under ...

Improving adversarial robustness requires revisiting misclassified examples. Y Wang; D Zou ; Learning imbalanced datasets with label-distribution-aware margin ...

Data-driven models are revealed to be vulnerable to adversarial examples, so improving the model's adversarial robustness has attracted extensive research.

Oct 22, 2024 · On the one hand, the balanced generalization processing penalizes the imbalance in the generation of adversarial variant in inner training, and ...

Data-driven models are revealed to be vulnerable to adversarial examples, so improving the model's adversarial robustness has attracted extensive research.

Balanced Adversarial Robust Learning for Industrial Fault Classification with Imbalanced Data. Zhenqin Yin, Xiaoyu Jiang, Jinchuan Qian, Xinmin Zhang ...

Feb 15, 2021 · We propose a new adversarial network for simultaneous classification and fault detection. In particular, we restore the balance in the imbalanced dataset by ...

Aug 7, 2020 · We propose a new adversarial network for simultaneous classification and fault detection. In particular, we restore the balance in the imbalanced dataset.

The purpose of this paper is to deeply explore the imbalance problems under various failure modes, and review and analyze the research methods and results ...