  This paper proposes \textit{layer fusion} - a model compression technique
that discovers which weights to combine and then fuses weights of similar
fully-connected, convolutional and attention layers. Layer fusion can
significantly reduce the number of layers of the original network with little
additional computation overhead, while maintaining competitive performance.
From experiments on CIFAR-10, we find that various deep convolution neural
networks can remain within 2\% accuracy points of the original networks up to a
compression ratio of 3.33 when iteratively retrained with layer fusion. For
experiments on the WikiText-2 language modelling dataset where pretrained
transformer models are used, we achieve compression that leads to a network
that is 20\% of its original size while being within 5 perplexity points of the
original network. We also find that other well-established compression
techniques can achieve competitive performance when compared to their original
networks given a sufficient number of retraining steps. Generally, we observe a
clear inflection point in performance as the amount of compression increases,
suggesting a bound on the amount of compression that can be achieved before an
exponential degradation in performance.
