Deep learning and Convolutional Neural Networks (CNNs) have driven major
transformations in diverse research areas. However, their limitations in
handling low-frequency information present obstacles in certain tasks like
interpreting global structures or managing smooth transition images. Despite
the promising performance of transformer structures in numerous tasks, their
intricate optimization complexities highlight the persistent need for refined
CNN enhancements using limited resources. Responding to these complexities, we
introduce a novel framework, the Multiscale Low-Frequency Memory (MLFM)
Network, with the goal to harness the full potential of CNNs while keeping
their complexity unchanged. The MLFM efficiently preserves low-frequency
information, enhancing performance in targeted computer vision tasks. Central
to our MLFM is the Low-Frequency Memory Unit (LFMU), which stores various
low-frequency data and forms a parallel channel to the core network. A key
advantage of MLFM is its seamless compatibility with various prevalent
networks, requiring no alterations to their original core structure. Testing on
ImageNet demonstrated substantial accuracy improvements in multiple 2D CNNs,
including ResNet, MobileNet, EfficientNet, and ConvNeXt. Furthermore, we
showcase MLFM's versatility beyond traditional image classification by
successfully integrating it into image-to-image translation tasks, specifically
in semantic segmentation networks like FCN and U-Net. In conclusion, our work
signifies a pivotal stride in the journey of optimizing the efficacy and
efficiency of CNNs with limited resources. This research builds upon the
existing CNN foundations and paves the way for future advancements in computer
vision. Our codes are available at this https URL MLFM.

使用 Multiscale Low-Frequency Memory (MLFM) 网络框架，通过有效地保存低频信息来提高卷积神经网络的性能。测试结果表明，MLFM 能够显著提高多种 2D CNNs 和语义分割网络在图像分类和图像翻译任务方面的准确性和效率。

多尺度低频记忆网络用于改进卷积神经网络中的特征提取

Multiscale Low-Frequency Memory Network for Improved Feature Extraction  in Convolutional Neural Networks

The vulnerability of deep neural networks to adversarial samples has been a
major impediment to their broad applications, despite their success in various
fields. Recently, some works suggested that adversarially-trained models
emphasize the importance of low-frequency information to achieve higher
robustness. While several attempts have been made to leverage this frequency
characteristic, they have all faced the issue that applying low-pass filters
directly to input images leads to irreversible loss of discriminative
information and poor generalizability to datasets with distinct frequency
features. This paper presents a plug-and-play module called the Frequency
Preference Control Module that adaptively reconfigures the low- and
high-frequency components of intermediate feature representations, providing
better utilization of frequency in robust learning. Empirical studies show that
our proposed module can be easily incorporated into any adversarial training
framework, further improving model robustness across different architectures
and datasets. Additionally, experiments were conducted to examine how the
frequency bias of robust models impacts the adversarial training process and
its final robustness, revealing interesting insights.

该研究针对深度神经网络在对抗样本上的脆弱性，提出了一种名为频率偏好控制模块的插件，它能够自适应地重新配置中间特征表示的低频和高频成分，以更好地利用频率进行鲁棒学习。实证研究表明，这种模块可以轻松地与任何对抗训练框架相结合，在不同体系结构和数据集上进一步提高模型的鲁棒性。此外，还进行了实验，以检验鲁棒模型的频率偏差如何影响对抗训练过程及其最终的鲁棒性，并揭示了有趣的见解。