  Weight initialization plays an important role in neural network training.
Widely used initialization methods are proposed and evaluated for networks that
are trained from scratch. However, the growing number of pretrained models now
offers new opportunities for tackling this classical problem of weight
initialization. In this work, we introduce weight selection, a method for
initializing smaller models by selecting a subset of weights from a pretrained
larger model. This enables the transfer of knowledge from pretrained weights to
smaller models. Our experiments demonstrate that weight selection can
significantly enhance the performance of small models and reduce their training
time. Notably, it can also be used together with knowledge distillation. Weight
selection offers a new approach to leverage the power of pretrained models in
resource-constrained settings, and we hope it can be a useful tool for training
small models in the large-model era. Code is available at
https://github.com/OscarXZQ/weight-selection.
