Keywords: Representation Learning, Representation Comparison, Functional, Experimental, Machine Learning, Computer Vision
TL;DR: Explores a representational functional similarity technique and yields perplexing results while encouraging a new paradigm of representational comparisons in the future despite this setback.
Abstract: Model stitching (Lenc \& Vedaldi 2015) is a compelling methodology to compare different neural network representations, because it allows us to measure to what degree they may be interchanged. We expand on a previous work from Bansal, Nakkiran \& Barak which used model stitching to compare representations of the same shapes learned by differently seeded and/or trained neural networks of the same architecture. Our contribution enables us to compare the representations learned by layers with different shapes from neural networks with different architectures. We subsequently reveal unexpected behavior of model stitching. Namely, we find that stitching, based on convolutions, for small ResNets, can reach high accuracy if those layers come later in the first (sender) network than in the second (receiver), even if those layers are far apart. This leads us to hypothesize that stitches are not in fact learning to match the representations expected by receiver layers, but instead finding different representations which nonetheless yield similar results. Thus, we believe that model stitching may not necessarily always be an accurate measure of similarity.