The Impact of Model Zoo Size and Composition on Weight Space Learning

Re-using trained neural network models is a common strategy to reduce training cost and transfer knowledge. Weight space learning - using the weights of trained models as data modality - is a promising new field to re-use populations of pre-trained models for future tasks. Approaches in this field have demonstrated high performance both on model analysis and weight generation tasks. However, until now their learning setup requires homogeneous model zoos where all models share the same exact architecture, limiting their capability to generalize beyond the population of models they saw during training. In this work, we remove this constraint and propose a modification to a common weight space learning method to accommodate training on heterogeneous populations of models. We further investigate the resulting impact of model diversity on generating unseen neural network model weights for zero-shot knowledge transfer. Our extensive experimental evaluation shows that including models with varying underlying image datasets has a high impact on performance and generalization, for both in-and out-of-distribution settings. When training neural networks for computer vision applications, we follow a dominant paradigm of pre-training and fine-tuning (Pan & Y ang, 2010; Y osinski et al., 2014), either by using pre-trained models trained from single datasets (Mensink et al., 2021) or pre-trained foundation models, which can be used for fine-tuning to multiple downstream tasks (Bommasani et al., 2021; Qiu et al., 2024). Given the vast amounts of pre-trained models, which have been deployed and released publicly on platforms such as Pytorch Hub or Huggingface, the research community has extended this paradigm by proposing the transfer or distillation of knowledge not only from one model but rather from a collection or population of pre-trained models.