We propose StitchNet, a novel neural network creation paradigm that stitches
together fragments (one or more consecutive network layers) from multiple
pre-trained neural networks. StitchNet allows the creation of high-performing
neural networks without the large compute and data requirements needed under
traditional model creation processes via backpropagation training. We leverage
Centered Kernel Alignment (CKA) as a compatibility measure to efficiently guide
the selection of these fragments in composing a network for a given task
tailored to specific accuracy needs and computing resource constraints. We then
show that these fragments can be stitched together to create neural networks
with comparable accuracy to traditionally trained networks at a fraction of
computing resource and data requirements. Finally, we explore a novel
on-the-fly personalized model creation and inference application enabled by
this new paradigm.

StitchNet 是一种新的神经网络创造范例，它能够将来自多个预训练神经网络的碎片（一个或多个连续的网络层）拼接在一起，使用 CKA 作为兼容性度量，以在特定的精度需求和计算资源限制下，有效地指导这些数据片段的选择，并实现可与传统训练网络相媲美的精度，同时使用计算资源和数据量的一小部分，从而探索了一种新的即兴个性化模型创建和推断应用。