In practice, we usually need to build variable-sized models adapting for
diverse resource constraints in different application scenarios, where weight
initialization is an important step prior to training. The Learngene framework,
introduced recently, firstly learns one compact part termed as learngene from a
large well-trained model, after which learngene is expanded to initialize
variable-sized models. In this paper, we start from analysing the importance of
guidance for the expansion of well-trained learngene layers, inspiring the
design of a simple but highly effective Learngene approach termed SWS
(Stage-wise Weight Sharing), where both learngene layers and their learning
process critically contribute to providing knowledge and guidance for
initializing models at varying scales. Specifically, to learn learngene layers,
we build an auxiliary model comprising multiple stages where the layer weights
in each stage are shared, after which we train it through distillation.
Subsequently, we expand these learngene layers containing stage information at
their corresponding stage to initialize models of variable depths. Extensive
experiments on ImageNet-1K demonstrate that SWS achieves consistent better
performance compared to many models trained from scratch, while reducing around
6.6x total training costs. In some cases, SWS performs better only after 1
epoch tuning. When initializing variable-sized models adapting for different
resource constraints, SWS achieves better results while reducing around 20x
parameters stored to initialize these models and around 10x pre-training costs,
in contrast to the pre-training and fine-tuning approach.

建立适应不同应用场景中多样资源约束的变量大小模型，权重初始化是训练前的重要步骤。Learngene 框架通过从大型已训练模型中学习一部分紧凑的 Learngene，再将其扩展用于初始化变量大小模型。本文分析了指导已训练 Learngene 层扩展的重要性，提出了一种称为 SWS（逐阶段权重共享）的简单而高效的 Learngene 方法，其中 Learngene 层和其学习过程对于在不同规模上初始化模型提供知识和指导。在 ImageNet-1K 上的大量实验证明，SWS 相对于从头开始训练的许多模型，性能更好且减少了约 6.6 倍的总训练成本。在某些情况下，SWS 经过 1 轮调优后效果更好。当初始化适应不同资源约束的变量大小模型时，与预训练和微调方法相比，SWS 在减少约 20 倍的存储参数以及约 10 倍的预训练成本的同时取得更好的结果。