  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.
