The current learning process of deep learning, regardless of any deep neural
network (DNN) architecture and/or learning algorithm used, is essentially a
single resolution training. We explore multiresolution learning and show that
multiresolution learning can significantly improve robustness of DNN models for
both 1D signal and 2D signal (image) prediction problems. We demonstrate this
improvement in terms of both noise and adversarial robustness as well as with
small training dataset size. Our results also suggest that it may not be
necessary to trade standard accuracy for robustness with multiresolution
learning, which is, interestingly, contrary to the observation obtained from
the traditional single resolution learning setting.

当前的深度学习学习过程，无论使用何种深度神经网络 (DNN) 架构和 / 或学习算法，本质上是单分辨率训练。我们探索多分辨率学习并表明，多分辨率学习可以显著提高 DNN 模型在一维信号和二维信号 (图像) 预测问题中的鲁棒性。我们不仅在噪声和对抗性鲁棒性方面展示了这种改进，而且在小训练数据集大小情况下也得到了改进。我们的结果还表明，通过多分辨率学习，不必以标准准确度换取鲁棒性，这与传统的单分辨率学习环境的观察结果相反。

通过多分辨率学习提高深度卷积神经网络的稳健性

Improving Robustness of Deep Convolutional Neural Networks via  Multiresolution Learning

Tasks that involve high-resolution dense prediction require a modeling of
both local and global patterns in a large input field. Although the local and
global structures often depend on each other and their simultaneous modeling is
important, many convolutional neural network (CNN)-based approaches interchange
representations in different resolutions only a few times. In this paper, we
claim the importance of a dense simultaneous modeling of multiresolution
representation and propose a novel CNN architecture called densely connected
multidilated DenseNet (D3Net). D3Net involves a novel multidilated convolution
that has different dilation factors in a single layer to model different
resolutions simultaneously. By combining the multidilated convolution with the
DenseNet architecture, D3Net incorporates multiresolution learning with an
exponentially growing receptive field in almost all layers, while avoiding the
aliasing problem that occurs when we naively incorporate the dilated
convolution in DenseNet. Experiments on the image semantic segmentation task
using Cityscapes and the audio source separation task using MUSDB18 show that
the proposed method has superior performance over state-of-the-art methods.

本文介绍了一种新的用于高分辨率密集预测的卷积神经网络架构，称为 D3Net，其采用了多分辨率学习和逐渐膨胀的感受野来同时建模局部和全局模式，避免了深度网络中的深度耗散问题，并在图像和音频处理任务中取得了优越的性能。