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。