  Exploring dense connectivity of convolutional operators establishes critical
"synapses" to communicate feature vectors from different levels and enriches
the set of transformations on Computer Vision applications. Yet, even with
heavy-machinery approaches such as Neural Architecture Search (NAS),
discovering effective connectivity patterns requires tremendous efforts due to
either constrained connectivity design space or a sub-optimal exploration
process induced by an unconstrained search space. In this paper, we propose
CSCO, a novel paradigm that fabricates effective connectivity of convolutional
operators with minimal utilization of existing design motifs and further
utilizes the discovered wiring to construct high-performing ConvNets. CSCO
guides the exploration via a neural predictor as a surrogate of the
ground-truth performance. We introduce Graph Isomorphism as data augmentation
to improve sample efficiency and propose a Metropolis-Hastings Evolutionary
Search (MH-ES) to evade locally optimal architectures and advance search
quality. Results on ImageNet show ~0.6% performance improvement over
hand-crafted and NAS-crafted dense connectivity. Our code is publicly
available.
