  Domain Generalization (DG) aims to generalize to arbitrary unseen domains. A
promising approach to improve model generalization in DG is the identification
of flat minima. One typical method for this task is SWAD, which involves
averaging weights along the training trajectory. However, the success of weight
averaging depends on the diversity of weights, which is limited when training
with a small learning rate. Instead, we observe that leveraging a large
learning rate can simultaneously promote weight diversity and facilitate the
identification of flat regions in the loss landscape. However, employing a
large learning rate suffers from the convergence problem, which cannot be
resolved by simply averaging the training weights. To address this issue, we
introduce a training strategy called Lookahead which involves the weight
interpolation, instead of average, between fast and slow weights. The fast
weight explores the weight space with a large learning rate, which is not
converged while the slow weight interpolates with it to ensure the convergence.
Besides, weight interpolation also helps identify flat minima by implicitly
optimizing the local entropy loss that measures flatness. To further prevent
overfitting during training, we propose two variants to regularize the training
weight with weighted averaged weight or with accumulated history weight. Taking
advantage of this new perspective, our methods achieve state-of-the-art
performance on both classification and semantic segmentation domain
generalization benchmarks. The code is available at
https://github.com/koncle/DG-with-Large-LR.
