Title: CNN Compression and Search Using Set Transformations with Width Modifiers on Network Architectures. Abstract: We propose a new approach, based on discrete filter pruning, to adapt off-the-shelf models into an embedded
environment. Importantly, we circumvent the usually prohibitive costs of model compression. Our method, Structured
Coarse Block Pruning (SCBP), prunes whole CNN kernels using width modifiers applied to a novel transformation of
convlayers into superblocks. SCBP uses set representations to construct a rudimentary search to provide candidate
networks. To test our approach, the original ResNet architectures serve as the baseline and also provide the 'seeds'
for our candidate search. The search produces a configurable number of compressed (derived) models. These derived models
are often ~20\% faster and ~50\% smaller than their unmodified counterparts. At the expense of accuracy, the size can
become even smaller and the inference latency lowered even further. The unique SCBP transformations yield many new model
variants, each with their own trade-offs, and does not require GPU clusters or expert humans for training and design.