论文标题
部分可观测时空混沌系统的无模型预测
Adversarially robust segmentation models learn perceptually-aligned gradients
论文作者
论文摘要
对抗性训练对语义分割网络的影响尚未得到彻底探讨。虽然先前的工作表明，对抗训练的图像分类器可用于执行图像合成，但我们尚未了解如何最好地利用受对抗训练的分割网络来执行相同的操作。使用简单的优化器，我们证明了经过对抗训练的语义分割网络可用于执行图像介入和生成。我们的实验表明，对抗训练的分割网络更健壮，并且确实表现出感知一致的梯度，有助于产生合理的图像构图。我们试图将额外的重量放在以下假设的背后，即对抗强大的模型表现出与人类视力更感知一致的梯度。通过图像综合，我们认为，感知一致的梯度可以更好地理解神经网络的学会表示，并有助于使神经网络更容易解释。
The effects of adversarial training on semantic segmentation networks has not been thoroughly explored. While previous work has shown that adversarially-trained image classifiers can be used to perform image synthesis, we have yet to understand how best to leverage an adversarially-trained segmentation network to do the same. Using a simple optimizer, we demonstrate that adversarially-trained semantic segmentation networks can be used to perform image inpainting and generation. Our experiments demonstrate that adversarially-trained segmentation networks are more robust and indeed exhibit perceptually-aligned gradients which help in producing plausible image inpaintings. We seek to place additional weight behind the hypothesis that adversarially robust models exhibit gradients that are more perceptually-aligned with human vision. Through image synthesis, we argue that perceptually-aligned gradients promote a better understanding of a neural network's learned representations and aid in making neural networks more interpretable.
