Core Concepts
The hierarchical Hopfield network implies a novel generalization of the MLP-Mixer model, called iMixer, which involves MLP layers that propagate forward from the output side to the input side. iMixer is an example of an invertible, implicit and iterative mixing module.
Abstract
The paper proposes a new direction for MetaFormer model design, facilitated by the novel Hopfield/Mixer correspondence. It theoretically derives a specific new MetaFormer model, called iMixer, based on the Hopfield/Mixer correspondence.
The key highlights are:
- iMixer naturally incorporates an implicit module (1-F)^-1, which may initially appear unconventional from a computer vision perspective.
- The theoretical formulation of iMixer is based on the hierarchical Hopfield network, which suggests a correspondence between Hopfield networks and Mixer models.
- Empirical experiments show that iMixer, despite its unique architecture, exhibits stable learning capabilities and achieves performance comparable to or better than the baseline vanilla MLP-Mixer on image classification tasks.
- The results imply that the correspondence between the Hopfield networks and the Mixer models serves as a principle for understanding a broader class of Transformer-like architecture designs.
Stats
The paper does not provide any specific numerical data or metrics to support the key logics. The results are presented in the form of comparative performance on image classification tasks.
Quotes
The paper does not contain any striking quotes supporting the key logics.