  Learning in weight spaces, where neural networks process the weights of other
deep neural networks, has emerged as a promising research direction with
applications in various fields, from analyzing and editing neural fields and
implicit neural representations, to network pruning and quantization. Recent
works designed architectures for effective learning in that space, which takes
into account its unique, permutation-equivariant, structure. Unfortunately, so
far these architectures suffer from severe overfitting and were shown to
benefit from large datasets. This poses a significant challenge because
generating data for this learning setup is laborious and time-consuming since
each data sample is a full set of network weights that has to be trained. In
this paper, we address this difficulty by investigating data augmentations for
weight spaces, a set of techniques that enable generating new data examples on
the fly without having to train additional input weight space elements. We
first review several recently proposed data augmentation schemes %that were
proposed recently and divide them into categories. We then introduce a novel
augmentation scheme based on the Mixup method. We evaluate the performance of
these techniques on existing benchmarks as well as new benchmarks we generate,
which can be valuable for future studies.
