  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 accuracy comparable to that of 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. The code is available at
https://github.com/steerapi/stitchnet.
