RepAn: Enhanced Annealing Through Re-parameterization

The simulated annealing algorithm aims to improve model convergence through multiple restarts of training. However, existing annealing algorithms overlook the cor-relation between different cycles, neglecting the potential for incremental learning. We contend that a fixed network structure prevents the model from recognizing distinct features at different training stages. To this end, we propose RepAn, redesigning the irreversible re-parameterization (Rep) method and integrating it with annealing to enhance training. Specifically, the network goes through Rep, ex-pansion, restoration, and backpropagation operations during training, and iterating through these processes in each annealing round. Such a method exhibits good generalization and is easy to apply, and we provide theoretical expla-nations for its effectiveness. Experiments demonstrate that our method improves baseline performance by 6.38% on the CIFAR-100 dataset and 2.80% on ImageNet, achieving state-of-the-art performance in the Rep field. The code is available at https://github.com/xfey/RepAn.