MLP-like models built entirely upon multi-layer perceptrons have recently
been revisited, exhibiting the comparable performance with transformers. It is
one of most promising architectures due to the excellent trade-off between
network capability and efficiency in the large-scale recognition tasks.
However, its generalization performance to heterogeneous tasks is inferior to
other architectures (e.g., CNNs and transformers) due to the extensive
retention of domain information. To address this problem, we propose a novel
frequency-aware MLP architecture, in which the domain-specific features are
filtered out in the transformed frequency domain, augmenting the invariant
descriptor for label prediction. Specifically, we design an adaptive Fourier
filter layer, in which a learnable frequency filter is utilized to adjust the
amplitude distribution by optimizing both the real and imaginary parts. A
low-rank enhancement module is further proposed to rectify the filtered
features by adding the low-frequency components from SVD decomposition.
Finally, a momentum update strategy is utilized to stabilize the optimization
to fluctuation of model parameters and inputs by the output distillation with
weighted historical states. To our best knowledge, we are the first to propose
a MLP-like backbone for domain generalization. Extensive experiments on three
benchmarks demonstrate significant generalization performance, outperforming
the state-of-the-art methods by a margin of 3%, 4% and 9%, respectively.

提出了一种新颖的基于自适应傅里叶滤波器层和低秩增强模块的多层感知器结构，以提高 MLP 模型的领域适应性，实现了比其他方法更好的泛化性能。

FAMLP: 一种面向领域泛化的频率感知 MLP-Like 架构

FAMLP: A Frequency-Aware MLP-Like Architecture For Domain Generalization

Recently, MLP-like vision models have achieved promising performances on
mainstream visual recognition tasks. In contrast with vision transformers and
CNNs, the success of MLP-like models shows that simple information fusion
operations among tokens and channels can yield a good representation power for
deep recognition models. However, existing MLP-like models fuse tokens through
static fusion operations, lacking adaptability to the contents of the tokens to
be mixed. Thus, customary information fusion procedures are not effective
enough. To this end, this paper presents an efficient MLP-like network
architecture, dubbed DynaMixer, resorting to dynamic information fusion.
Critically, we propose a procedure, on which the DynaMixer model relies, to
dynamically generate mixing matrices by leveraging the contents of all the
tokens to be mixed. To reduce the time complexity and improve the robustness, a
dimensionality reduction technique and a multi-segment fusion mechanism are
adopted. Our proposed DynaMixer model (97M parameters) achieves 84.3\% top-1
accuracy on the ImageNet-1K dataset without extra training data, performing
favorably against the state-of-the-art vision MLP models. When the number of
parameters is reduced to 26M, it still achieves 82.7\% top-1 accuracy,
surpassing the existing MLP-like models with a similar capacity. The code is
available at https://github.com/ziyuwwang/DynaMixer.

本文提出了一个称为 Dyn Mixer 的高效 MLP-like 深度学习网络结构，用于动态信息融合，并且通过一个过程生成融合矩阵，以应对 tokens 的内容，并通过采用降维技术和多段融合机制来减少时间复杂度和提高鲁棒性。Dyn Mixer 模型不需要额外的训练数据，在 ImageNet-1K 数据集上达到了 84.3％的准确率，参数数量减少到 26M 时，仍然获得 82.7％的 top-1 准确率，胜过类似容量的现有 MLP-like 模型。