论文标题
部分可观测时空混沌系统的无模型预测
CorrGAN: Input Transformation Technique Against Natural Corruptions
论文作者
论文摘要
由于深度神经网络（DNN）在不同任务上的准确性越来越高，因此许多实时系统都在使用DNN。这些DNN容易受到对抗性扰动和腐败的影响。具体而言，雾，模糊，对比等的自然腐败会影响自动驾驶汽车中DNN的预测。实时需要检测到这些损坏，并且还需要对损坏的输入进行正确的预测。在这项工作中，我们提出了Corrgan方法，该方法可以在提供损坏的输入时产生良性输入。在此框架中，我们使用新型的基于中间输出的损耗函数来训练生成对抗网络（GAN）。 GAN可以将损坏的输入变态并生成良性输入。通过实验，我们表明，使用Corrgan可以通过DNN正确对多达75.2％的损坏错误分类输入进行分类。
Because of the increasing accuracy of Deep Neural Networks (DNNs) on different tasks, a lot of real times systems are utilizing DNNs. These DNNs are vulnerable to adversarial perturbations and corruptions. Specifically, natural corruptions like fog, blur, contrast etc can affect the prediction of DNN in an autonomous vehicle. In real time, these corruptions are needed to be detected and also the corrupted inputs are needed to be de-noised to be predicted correctly. In this work, we propose CorrGAN approach, which can generate benign input when a corrupted input is provided. In this framework, we train Generative Adversarial Network (GAN) with novel intermediate output-based loss function. The GAN can denoise the corrupted input and generate benign input. Through experimentation, we show that up to 75.2% of the corrupted misclassified inputs can be classified correctly by DNN using CorrGAN.
