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About this paper
Naoki Sato, Hideaki Iiduka
A new Deep Equilibrium Model architecture, Lipschitz MDEQ, guarantees fixed-point convergence for both forward and backward passes by controlling its Lipschitz constant. This method substantially accelerates training and inference, achieving up to 6.43x speed-up while allowing for a tunable balance between accuracy and computational cost.
Key insights
- Explicitly controlling the Lipschitz constant of a DEQ's fixed-point mapping ensures guaranteed and accelerated fixed-point convergence for both forward and backward passes.
- Specific architectural components, such as Mean-Only Group Normalization, are crucial for effectively imposing contraction properties within deep implicit networks.
- A tunable Lipschitz constant provides a direct method to balance model accuracy against computational speed, offering practical flexibility for various applications.
Summary by alphaXiv.
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